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Data Architect Department: Data Employment Type: Contract Location: London Reporting To: Head of Data Description This role can be filled via fixed term contract or via daily rate (Inside IR35) via umbrella company. Initially 3 months with the potential for expansion. Role Overview This is a focused, high-value engagement to lead the data architecture of Defaqto's redesigned market data model. We have an initial view of the logical structure we need, and we are looking for an experienced architect to test, challenge, and refine that thinking - then translate it into a signed off logical model and implementation recommendations for the CTO and Head of Data to act on. What you'll do Context: We have developed an initial view of the logical architecture - centred on four core entities: Package, Component, Attribute, and Market - but this is a starting point, not a fixed brief. We are looking for an architect who will interrogate that thinking, validate it against real business requirements, and produce a model that genuinely fits how our data needs to work. The initial scope is the market data product estate and the primary research data platform. Technology choices for physical implementation have not been finalised. Google BigQuery is our existing lakehouse platform and the likely foundation, but decisions on the transformation layer, tooling, and overall implementation approach will be made during this engagement in collaboration with the CTO and Head of Data. The logical model itself is a technology agnostic deliverable; implementation recommendations are expected alongside it. The Engagement: This contract covers the design and architecture phase of the data model project (Phase 1 of a phased delivery programme). The primary output is a signed off logical data model and a technology implementation recommendation, produced in close collaboration with the CTO and Head of Data. The logical model is a technology agnostic deliverable; the implementation recommendation should be grounded in Defaqto's existing technology landscape and make a clear case for the chosen approach. A Senior Data Engineer - permanent hire, to be recruited, will own the build and ongoing implementation once the architecture is agreed. The architect's role is to design the model, recommend the implementation approach, and provide sufficient documentation that the engineering team can execute without ongoing dependency on the contractor. Scope & Constraints In Scope Logical model design for the market data product estate - core entities, relationships, and attributes Technology implementation recommendation - physical implementation approach suited to the existing stack, presented to CTO and Head of Data Compatibility view specification for research platform continuity during transition Business rule formalisation for deduplication, ratings hierarchy, and attribute priority logic Stakeholder workshops and sign off facilitation Data dictionary and handover documentation Out of Scope Transformation layer build and implementation (owned by Senior Data Engineer) Research platform frontend adaptation (Phase 2 workstream) Full estate migration beyond the initial market data scope (Phase 2+) Ongoing data governance or catalogue ownership Graph database design or implementation What you'll need to succeed Essential requirements Demonstrable experience leading logical and physical data model design for analytical data warehouses Strong command of entity relationship modelling, normalisation, and dimensional design Experience with cloud data warehouse platforms - Google BigQuery is the existing lakehouse technology; hands on experience with BigQuery or equivalent (Snowflake, Redshift, Databricks) is expected Ability to produce technology agnostic logical models and translate them into well reasoned implementation recommendations Proven ability to facilitate workshops with mixed technical and non technical stakeholders and produce signed off artefacts Experience producing data dictionaries and technical documentation to a standard usable by engineering teams Comfortable presenting architecture options and trade offs to senior technology leadership Ability to work independently and manage delivery to milestone based timelines Desirable requirements Familiarity with dbt - sufficient to design a schema that a dbt based transformation layer can implement effectively Experience designing compatibility or migration layers for legacy platform transitions Exposure to data cataloguing tools (Google Dataplex, DataHub, Atlan) Understanding of product data or market intelligence data structures Experience working within a phased, non big bang migration approach Important to know Location: This is a hybrid working role usually with 2 days each week in the office - office base can be London or Haddenham. From time to time if there are workshops, you may need to be in the office more frequently. Right to Work Applicants must already hold a legal right to work in the UK without time restrictions and without the need for future sponsorship. We are unable to provide Skilled Worker visa sponsorship.

The opportunity We are seeking a skilled and experienced Platform Engineer to join our growing platform engineering team. The successful applicant will have a strong background in building and/or deploying data platforms across a range of technology stacks that will include Databricks on at least one of MS Azure, AWS and GCP using Infrastructure as Code and modern DevOps processes. The role will require experience of integrating data ingestion frameworks/tooling and orchestration technologies to facilitate data transfer between source systems and a target platform. About Kubrick Kubrick is an award-winning consultancy that helps organizations design, build and scale data and AI solutions that deliver lasting impact. We work with organizations across industries including financial services, healthcare, retail, and the public sector, and partner with leading technology providers such as Microsoft, Snowflake, Databricks, and Collibra. What sets us apart is how we build capabilities and careers. Through continuous learning, real world experience, and a collaborative culture, we help our people develop the skills, confidence, and experience to build rewarding careers. What you'll be doing Lead technical delivery of a Kubrick team deployed on client project engagements, ensuring that Kubrick is known for the quality of the technical solutions it provides. Work with Kubrick & client staff of other disciplines to understand and assess requirements, support solution design activities and inform delivery planning. Seek, build, and maintain effective client relationships contributing to Kubrick's commercial priorities and supporting our partnering approach to working with clients. Participating in self directed or group learning and upskilling (gaining Kubrick funded certifications) to ensure your technical skills stay up to date and industry relevant. What we're looking for Experience designing and implementing cloud native solutions on at least one of AWS, Azure or Google Cloud (experience with a second is desirable). Implementation experience deploying cloud resources and workloads to a major hyperscaler (Azure, AWS or GCP) across more than one project. Experience deploying at least one greenfield project through to Production. Solid Terraform development skills, including writing reusable, modular code. You should be comfortable setting up and configuring a Terraform development environment and the associated providers, including authentication. Experience configuring and running Terraform PR and CI/CD pipelines using DevOps tooling such as GitHub Actions or Azure DevOps, applying DevOps best practice (source control, Infrastructure as Code, CI/CD). Comfortable with core cloud/software networking concepts, with hands on experience building or maintaining networking components in your environments. Experience with Cloud Identity and Access Management, including configuring identities and permissions in at least one major hyperscaler. You should be able to explain the differences between common authentication mechanisms and identity types. Good working knowledge of security concepts such as encryption and secret management, and familiarity with concepts like Principle of Least Privilege and Segregation of Duties. Working knowledge of monitoring, logging and observability patterns in cloud environments, and able to articulate their benefits. Strong analytical and troubleshooting skills for diagnosing and resolving infrastructure and platform issues. Ability to work with stakeholders or clients to translate requirements into technical solutions. Desirable Experience across two or more of AWS, Azure and GCP. Experience working in or leading teams maintaining Production environments in enterprise settings with established, comprehensive change control processes. Experience with automated testing of infrastructure, including recommended approaches for testing Terraform changes. Familiarity with Zero Trust Architecture and the ability to advise customers on the relative pros and cons of different identity/security approaches. Hands on experience deploying modern data platforms and data ingestion capabilities (e.g., Databricks or Snowflake). Experience in delivery leadership, including leading small delivery teams. Benefits 33 days annual leave (25 days annual leave plus 8 public holidays) Pension matching up to 5% Unlimited mental health support through Enhanced paid family leave Additional family leave support, including fertility treatment leave and dependent care leave Financial wellbeing support Cycle to Work scheme Specsavers eye care vouchers Flexible working options Internal mobility and career development opportunities Recognition and reward programs Learning and development opportunities, including professional certifications Employee Assistance Program (EAP) Mental health, diversity and inclusion support We operate a hybrid working model, with employees typically spending 2-3 days per week in our London office alongside client meetings and remote working. Diversity & inclusion We're committed to building a more diverse and inclusive tech industry and encourage applications from people currently underrepresented in data and AI, including women, people from underrepresented racial and ethnic backgrounds, LGBTQ+ people, people with disabilities, and people who are neurodivergent. If you need any adjustments during the recruitment process, just let us know. We're here to support you.

Solutions Engineer - EMEA The Solutions Engineer supports technical sales across the EMEA region, demonstrating how ThoughtSpot's products can drive business value for prospects and existing customers. The role functions as a trusted technical advisor throughout the sales process, partnering closely with Account Executives and stakeholders to solve complex data and analytics challenges. Key Responsibilities Understand customers' analytics goals, technical environments, and business challenges through discovery across enterprise accounts. Present and articulate the business value of the platform to technical, business, and executive stakeholders. Demonstrate the product, differentiating ThoughtSpot from competitors. Define and conduct product evaluations with customers, analyzing their own data and validating business value. Partner with Sales to support account strategy, deal progression, and technical win plans for strategic EMEA opportunities. Develop trusted advisor relationships with customer champions and Account Executives, aligning all activities with customer and company success. Manage multiple customer engagements simultaneously while maintaining high responsiveness, quality, and execution. Learn, share knowledge, and contribute to best practices across the Solutions Engineering organization. Leverage generative AI and advanced automation to personalize customer engagements at scale. Experiment with emerging tools (conversational AI, autonomous agents, predictive analytics, AI powered deal coaching) and share best practices. Use AI driven insights to craft personalized technical messaging tailored to each prospect's business challenges, industry, and decision making priorities. Continuously refine demonstrations, evaluations, and messaging using AI powered analytics and engagement insights. Qualifications & Skills Strong verbal and written communication skills in English and French. 2-3+ years of experience in Solutions Engineering, Sales Engineering, Solutions Consulting, or an analytics role supporting enterprise sales across EMEA. Proficiency in SQL; working knowledge of JavaScript or Python preferred. Solid understanding of data modeling, cloud data platforms, and BI/analytics tools (Snowflake, Redshift, BigQuery, Synapse, Tableau, Looker, etc.). Ability to understand customer needs, solve technical and business challenges, and clearly communicate value to technical, business, and executive audiences. Superior presentation and demonstration skills; ability to articulate benefits of cloud and analytics technologies. Experience using AI and automation to improve technical discovery, demo personalization, evaluation workflows, and solution positioning. Track record of driving measurable results through AI and data driven insights in customer facing technical engagements. Highly motivated, coachable, analytical, and adaptable; able to thrive independently in a fast paced environment. Able to travel as needed. AI literacy and workflow integration: confidence in integrating AI tools (LLMs) into daily workflow, automating routine tasks, and improving work quality. Hands on experience leveraging AI tools for research, content creation, and document summarization while maintaining judgment and final decisions. Effective prompt writing for accurate and creative AI results. Curiosity, adaptability, and critical thinking to determine when to use AI versus human judgment.

Data Modeler (Corporate Functions)Applyremote type: Hybrid - Remote and Onsitelocations: Belfast, Northern Irelandtime type: Full timeposted on: Posted Yesterdayjob requisition id: 26-480We are looking for a Data Modeler (Corporate Functions) who will join the Corporate Data Intelligence Team to drive data driven decisions. Overview of the Role You will serve as the critical bridge between business stakeholders and our data platform, deeply embedded in corporate functions including SAP ERP, Master Data, Salesforce (SFDC), Procurement, and Planning. This is a business-facing, domain-focused role with strong data engineering experience. You will design robust, scalable data models, drive requirement gathering sessions, and work hand-in-hand with the Senior Corporate Analytics Architect and the broader Corporate Data Intelligence function. You will report directly to the Corporate Data Intelligence Lead. Your Day-to-Day - We do what others say can't be done Lead and facilitate requirements gathering sessions with business stakeholders across Finance, Procurement, Planning, Supply Chain, and CRM functions Design and maintain conceptual, logical, and physical data models for corporate domains, SAP ERP, Master Data, SFDC, Procurement, and Planning Develop and manage dbt models (transformations, tests, documentation, macros) to support analytics and reporting use cases Partner closely with the Senior Corporate Analytics Architect to align data models with enterprise architecture standards and governance frameworks Serve as the domain SME, map source-to-target lineage from SAP, SFDC, and other enterprise systems with deep understanding of business rules and object hierarchies Translate complex business requirements into clear data model specifications, ER diagrams, and data dictionaries Ensure data consistency and integrity across domains through well-governed master data modelling practices Define and enforce data model standards, naming conventions, and documentation across the Corporate Data Intelligence domain Collaborate with analytics and BI teams to ensure models support self-service analytics, semantic layers, and reusable data assets Act as the liaison between business domain owners and the data platform team Your Profile - Ready to Join the Pack? Proven hands-on experience in data modelling - conceptual, logical, and physical including dimensional modelling and ER design Strong working knowledge of SAP ERP (FI/CO, MM, SD or equivalent modules), Master Data Management, Salesforce (SFDC), Procurement, and Planning processes Hands-on experience with dbt - model development, testing, documentation, and macros Advanced SQL skills with ability to profile, analyze, and validate data against business definitions Demonstrated experience in requirements gathering, running workshops, documenting user stories, and engaging senior business stakeholders confidently Strong analytical mindset, ability to define business metrics, validate data quality, and ensure model accuracy against KPIs Excellent communication and interpersonal skills - comfortable working with Senior Architects, business leads, and cross-functional teams Nice to Have Experience with Data Engineering concepts (ELT pipelines, Snowflake, Dagster) Exposure to Snowflake as the analytics warehouse platform Knowledge of SAP MDG (Master Data Governance) or equivalent MDM platforms Experience in a manufacturing, semiconductor, or high-tech industry environment Familiarity with Agile delivery methodology

Lead Data Engineer London, United Kingdom Posted about 1 year ago Compensation Competitive Role Type Contract Visa Sponsorship Not provided Benefits & perks To be disclosed Technical Stack Java Amazon AWS Artificial Intelligence Scala Python Cortex Qualifications & Experience Experience with Java, Python, Scala, and related open source platforms Understanding of AWS Bedrock, AI Services, and Snowflake Cortex services implementation lifecycle The latest tools are desirable

The Opportunity Join a team building the data foundations that support the firm's AI and analytics capabilities. This role sits within the engineering effort to develop a modern Lakehouse and AI data platform that enables reliable, well-governed and high-performing data use across the firm. At Goldman Sachs, engineering teams are positioned at the centre of the business, building scalable systems, solving complex technical problems and turning data into action. In data engineering roles, the emphasis is on designing, building and maintaining large-scale data platforms, delivering production pipelines, improving reliability and quality, and partnering closely with users of the platform. This is a delivery-focused role for engineers who want to build robust data assets in production, work with modern data technologies, and grow over time within the firm. You will contribute to the data models, pipelines and platform capabilities that underpin analytics, operational decision-making and emerging AI use cases, and may also help extend platform tooling where additional functionality is needed. Role Summary As a Data Engineer in the Lakehouse and AI Data Platform team, you will design, build, test and support data pipelines and curated datasets on the firm's modern data platform. You will work across ingestion, transformation, modelling, optimisation and data quality, helping to deliver data products that are reliable, scalable and fit for purpose. Where there are gaps in platform functionality, you may also contribute to shared tooling or framework components that improve how the platform is used and operated. The role is suited to engineers who are comfortable writing code, working with SQL and distributed data processing, and solving practical delivery problems in a team environment. More experienced candidates may also contribute to technical design, platform standards and the shaping of delivery approaches across a wider set of use cases. Key Responsibilities Pipeline Engineering Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform. Refactor or modernise existing data flows where needed to improve reliability, performance and maintainability. Where needed, build reusable tooling to improve delivery, consistency and operational support. Ensure data pipelines are production-ready, well tested and operationally supportable. Data Modelling and Curation Develop raw, refined and curated datasets that support analytics, reporting and AI use cases. Apply sound data modelling principles to represent business entities, relationships and historical change accurately. Work with consumers to shape data products that are usable, well documented and aligned to business needs. Data Quality and Reconciliation Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets. Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise. Contribute to clear standards for testing, monitoring and issue resolution. Contribute to practical improvements in testing, monitoring or reconciliation tooling where these strengthen platform reliability and day-to-day delivery. Delivery and Partnership Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations. Communicate clearly on progress, risks, dependencies and design choices, including where delivery would benefit from improvements to shared platform tooling. For more senior candidates, take a broader role in technical leadership, task breakdown and support for junior engineers. Skills and Experience Required Bachelor's or master's degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise. Strong hands on programming experience in Python or Java. Good working knowledge of SQL, including troubleshooting, optimisation and data analysis. Ability to learn new tools, internal platforms and delivery workflows quickly. Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices. Data Engineering Capability Understanding of temporal data modelling, including the handling of historical state and change over time. Knowledge of schema design, schema evolution and data compatibility considerations. Understanding of partitioning, clustering and other techniques used to improve data performance at scale. Ability to make sensible design choices across normalised and denormalised models, and between natural and surrogate keys. Practical approach to data quality, reconciliation and root-cause analysis. Experience building or supporting production data pipelines in a collaborative engineering environment. Experience working with distributed data processing frameworks such as Apache Spark. Working knowledge of common data formats such as JSON, Avro and Parquet. For More Experienced Candidates Stronger ownership of technical design across multiple datasets or pipeline domains. Experience guiding implementation standards, code quality and engineering practices within a team. Ability to lead delivery for a workstream, manage dependencies and support less experienced engineers. Technology Environment The role will involve working with a modern and evolving data stack. Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies. Examples of technologies in scope include: Data processing and logic: ANSI SQL, Apache Spark, Kafka Data formats: JSON, Avro, Parquet Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ Engineering and deployment: CI/CD tooling, containerised or Kubernetes-based deployment approaches where relevant What We Are Looking For We are looking for engineers who can deliver well-structured, reliable solutions in production and who take ownership of the quality of what they build. The role suits candidates who are technically strong, pragmatic and comfortable working in a fast-paced environment where data platforms support important business outcomes. It will also suit candidates who are willing to contribute to shared tooling or platform components that make the wider engineering environment more effective. Stronger candidates will typically demonstrate: sound judgement in technical trade-offs attention to detail in data correctness and testing a clear and structured approach to problem solving willingness to work closely with stakeholders and partner teams an ability to identify when delivery problems would be better solved through reusable tooling or platform improvements an interest in developing long-term expertise within the firm Equal Opportunity Employer Statement Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law. We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process.

As an experienced Analytics Engineer at Wrisk, you will be the bridge between raw data and impactful business insights. You will work closely with our Data Engineering team to manage the data pipeline while taking full ownership of the transformation layer, building robust, scalable data models and metrics that serve as the "single source of truth" for the entire organisation and our business partners. You will own our Business Intelligence and Analytics Stack, and lead the development of insightful reports and dashboards. This includes taking ownership of our external Analytics Products - a key USP of Wrisk. We are looking for a proactive professional who thrives when given a problem statement and the autonomy to deliver a finished solution, from initial data modelling through to the final reporting suite. What you'll do Data Modelling: Design, develop, and maintain well documented, tested, and flexible data models within our data warehouse. Stack Architecture: Develop and optimise our modern BI and analytics stack, ensuring data is clean, reliable, and performant. Metric Definitions: Maintain the logic for our business metrics across our semantic layer, ensuring they are defined consistently across all tools and departments. Pipeline Collaboration: Work with Data Engineering to identify and integrate key data sources, maintain accuracy and stability, and align the upstream data structures to support downstream analytics and reporting. Software Excellence: Utilise version control (Git), code reviews, and data quality testing to ensure the integrity of our analytics layer. Reporting: Design and build high quality, intuitive dashboards and visualisations that communicate complex data simply and effectively. Analytics Products: Take ownership of the maintenance and enhancement of Wrisk's external facing analytics products, ensuring they remain a high performing, reliable product offering for our partners. Self Service Enablement: Build intuitive data marts that empower Analysts and business users to perform their own analysis with confidence. Requirements Gathering: Partner directly with stakeholders in Commercial, Operations, Product, and across external partners to deeply understand their reporting needs and translate them into technical specifications. Autonomous Problem Solving: Independently identify and implement areas of opportunity in our stack and processes, and troubleshoot data and reporting issues. We expect you to be a self starter who manages your own roadmap and deliverables. Qualifications Experience: 4+ years in Analytics Engineering, Data Engineering, or a technical BI role with architecture experience. SQL Expertise: Advanced proficiency in SQL (CTEs, window functions, complex joins, and query optimisation). Visualisation Expertise: Significant experience building sophisticated, user friendly dashboards in modern BI tools (e.g., Looker, QuickSight, Tableau, or Power BI) with a strong eye for data storytelling. Modern Data Stack: Hands on experience with tools like dbt, Snowflake/BigQuery/Redshift, and Fivetran/Airbyte. Data Modelling: Strong understanding of data modelling best practice for modern analytics. Goal Oriented: A proven track record of working independently and delivering complex analytics projects from start to finish with minimal supervision. Stakeholder Management: Proven ability to collaborate with non technical business partners and external clients to gather requirements and explain technical trade offs. Desirable/advantageous skills and experience Experience working in a fast paced scale up environment. Knowledge of Python for data manipulation or automation. Financial Services or Insurance industry experience.

Sivara GmbH is seeking a Data & Analytics Lead based in Greater London. This permanent, hybrid role requires strong expertise in data warehousing, SQL, and cloud platforms such as Snowflake. Responsibilities include leading the design and delivery of technical solutions, mentoring team members, and collaborating with stakeholders to enable data-driven decision-making. The position offers a salary between £38,000 and £55,000, with flexible working arrangements.

Sivara GmbH is seeking a Data & Analytics Lead based in Greater London. This permanent, hybrid role requires strong expertise in data warehousing, SQL, and cloud platforms such as Snowflake. Responsibilities include leading the design and delivery of technical solutions, mentoring team members, and collaborating with stakeholders to enable data-driven decision-making. The position offers a salary between £38,000 and £55,000, with flexible working arrangements.

Salary: £38,000 - 55,000 per year Requirements Strong understanding of data warehousing principles, data modelling, and ETL/ELT patterns. Experience developing and maintaining complex data models. Advanced SQL skills, including query optimisation and performance tuning. Hands on experience with cloud data platforms such as Snowflake and Microsoft Fabric. Ability to extract meaningful insights from complex and diverse data sets. Ability to work in a hybrid arrangement, based 3 days a week at our Runcorn site. Experience supporting technical, cyber security, and regulatory standards across data assets. Responsibilities Provide technical leadership for the design and delivery of Data & Analytics solutions aligned to business priorities and industry best practice. Contribute to the definition and evolution of our Data & Analytics strategy, informed by emerging technology trends and hands on delivery experience. Lead by example through hands on development of high quality, sustainable solutions using cloud data platforms such as Snowflake and Microsoft Fabric. Collaborate closely with business stakeholders to understand processes and challenges and translate them into effective data and analytics solutions. Design, develop, and maintain robust data models, pipelines, and analytics products using industry standard tools and practices. Own the end to end project lifecycle, including research, solution design, documentation, configuration, testing, implementation, go live, and end user training. Mentor and support developers within the team, promoting high standards of engineering, documentation, and professional conduct. Ensure all data assets conform to technical, cyber security, and regulatory standards while meeting the needs of multiple business units. Enable the business to make confident, data driven decisions through reliable analytics delivery. Technologies Cloud data platforms (Snowflake, Microsoft Fabric) ETL/ELT pipelines SQL Cyber security & regulatory compliance This is a permanent, hybrid role based in our Runcorn office, with three days a week on site. Parking is available and commuting is easy from Liverpool, Cheshire, and South Manchester.

Data Engineer - Hybrid (Central Birmingham) - £45,000 - £65,000 + great benefits I'm looking for a Data Engineer for a leading, national business. They are looking for someone who is keen to get involved in companywide, data focused projects working with modern data tools. They need someone who is an all-rounder, able to work as part of a team but also independently at times. You'll be joining an established team in the middle of a companywide digital transformation. They are working to move to Snowflake environment, changing the way data is used across the business. You'll be building pipelines to support reporting tools and involved in a wide range of hands on work. As a Data Engineer, you'll be: Building and maintaining data pipelines (ETL/ELT) for Snowflake. Working with large datasets and integrating data from APIs, CSV files, and SQL Server. Supporting reporting tools like Power BI and SSRS. Helping design and maintain a scalable data warehouse. Collaborating with other teams to improve how data is stored, processed, and used. Supporting data governance and privacy efforts. Translating technical ideas into clear, practical solutions. What we need from the successful Data Engineer: Previous experience as a Data Engineer. Solid experience with Snowflake and Azure. Strong SQL skills and experience building ETL processes. Comfortable working with complex data flows and models. Someone who enjoys working with others and sharing ideas. Benefits for the Data Engineer: Opportunities to learn and grow through training. 28 days holiday + bank holiday. Health and wellbeing support, including free GP access and a health cash plan. Enhanced maternity pay and family friendly benefits. Discounts on gym memberships, car leasing, and mortgage services. Pension scheme and employee savings programmes. Access to a Health Toolkit with fitness, nutrition, and health resources. Hunter Selection Limited is a recruitment consultancy with offices UK wide, specialising in permanent & contract roles within Engineering & Manufacturing, IT & Digital, Science & Technology and Service & Sales sectors. Please note as we receive a high level of applications we can only respond to applicants whose skills & qualifications are suitable for this position. No terminology in this advert is intended to discriminate against any of the protected characteristics that fall under the Equality Act 2010. For the purposes of the Conduct Regulations 2003, when advertising permanent vacancies we are acting as an Employment Agency, and when advertising temporary/contract vacancies we are acting as an Employment Business. Job Tenure: Permanent Salary: 28+bank, health & wellbeing + more Location: Birmingham, West Midlands

Wagstaff Recruitment is seeking a Data Engineer for a hybrid role based in Central Birmingham. This position offers a competitive salary of £45,000 - £65,000, plus great benefits. You will be part of a dynamic team driving companywide data-focused projects, particularly in transitioning to a Snowflake environment. The successful candidate will have strong SQL skills and experience with Snowflake and Azure. Benefits include 28 days holiday, health support, and employee savings programs.

Salary: £38,000 - 55,000 per year Requirements Strong understanding of data warehousing principles, data modelling, and ETL/ELT patterns. Experience developing and maintaining complex data models. Advanced SQL skills, including query optimisation and performance tuning. Hands on experience with cloud data platforms such as Snowflake and Microsoft Fabric. Ability to extract meaningful insights from complex and diverse data sets. Ability to work in a hybrid arrangement, based 3 days a week at our Runcorn site. Experience supporting technical, cyber security, and regulatory standards across data assets. Responsibilities Provide technical leadership for the design and delivery of Data & Analytics solutions aligned to business priorities and industry best practice. Contribute to the definition and evolution of our Data & Analytics strategy, informed by emerging technology trends and hands on delivery experience. Lead by example through hands on development of high quality, sustainable solutions using cloud data platforms such as Snowflake and Microsoft Fabric. Collaborate closely with business stakeholders to understand processes and challenges and translate them into effective data and analytics solutions. Design, develop, and maintain robust data models, pipelines, and analytics products using industry standard tools and practices. Own the end to end project lifecycle, including research, solution design, documentation, configuration, testing, implementation, go live, and end user training. Mentor and support developers within the team, promoting high standards of engineering, documentation, and professional conduct. Ensure all data assets conform to technical, cyber security, and regulatory standards while meeting the needs of multiple business units. Enable the business to make confident, data driven decisions through reliable analytics delivery. Technologies Cloud data platforms (Snowflake, Microsoft Fabric) ETL/ELT pipelines SQL Cyber security & regulatory compliance This is a permanent, hybrid role based in our Runcorn office, with three days a week on site. Parking is available and commuting is easy from Liverpool, Cheshire, and South Manchester.

Role/Team overview Data science is at the heart of decision making in Sky and drives a majority of decisions across marketing, customer interactions and customer services. In the Advanced Analytics team we are scaling up our data science practice to transform how we manage customers across all life cycles for both experience profitability. We have built and successfully deployed intelligence into Sky and are already delivering realisable value into the business. We gather a very large amount of data on customer behaviours and interactions and are looking for data scientists to extract value from our data assets and maximise customer value. What you'll do: Producing high quality, actionable insights through advanced analytics and cutting edge predictive algorithms: work as a self starter and be actively curious about what numbers are telling us and how they can be actioned upon to improve our customer experience. Designing and implementing innovative and commercially led data science and insights roadmaps with stakeholders (marketing, retention, service, finance) to improve the way they make decisions: hypotheses led problem solving to deliver solutions to business problems. Engage with our technology teams and data engineers to build compliant, efficient and scalable solutions. Strengthen our internal data science capabilities by continuously innovating and driving new ideas, building technology partnerships with our vendors, training and developing our people and creating a world class data science team at Sky. Engage with key stakeholders (marketing, retention, service, finance) to understand business objectives and support these objectives through best use of advanced analytics approaches. Responsible for identifying the best analytical techniques, developing the solution, creating a framework for testing the solution and measuring the incremental value to the business from the solution. What you'll bring: Deep expertise in machine learning and statistical modelling, with proven impact in areas such as personalisation, churn, pricing, and optimisation. Ability to quickly understand a business objective and problem solve to create an analytical solution, and develop a solution alongside stakeholders - essential. Commercial knowledge and applications of data science to drive commercial value is a must - everything we do drives value into the business. Hands on experience with Generative AI and LLMs, including prompt engineering, fine tuning, evaluation, and production deployment. Strong command of modern data stacks, including Python, SQL, Spark, BigQuery, and Snowflake. Clear communication skills, able to explain complex AI concepts, including LLMs and agentic systems, to both technical and non technical audiences. Benefits and perks Sky Q, for the TV you love all in one place The magic of Sky Glass at an exclusive rate Discounted mobile and broadband A wide range of Sky VIP rewards and experiences We've adopted a hybrid working approach to give more flexibility on where and how we work. The hybrid working expectations for this role are 2 days in the office per week. Your office space Our Osterley Campus is a 10 minute walk from Syon Lane train station. Or you can hop on one of our free shuttle buses that run to and from Osterley, Gunnersbury, Ealing Broadway and South Ealing tube stations. There's also plenty of bike shelters and showers. On campus you'll find 13 subsidised restaurants, cafes and a Waitrose. You can keep in shape at our subsidised gym, catch the latest shows and movies at our cinema, get your car washed and even get pampered at our beauty salon. Just so you know: if your application is successful, we'll ask you to complete a criminal record check. And depending on the role you have applied for and the nature of any convictions you may have, we might have to withdraw the offer. Legal and Equality We're an equal opportunity employer and value diversity. We are a Disability Confident Accredited Employer, and welcome and encourage applications from all candidates. We will look to ensure a fair and consistent experience for all and will make reasonable adjustments to support you where appropriate. Please flag any adjustments you need as early as you can. To be eligible for this role you are required to have the appropriate right to work in the UK. Please be aware Sky does not offer sponsorship for this position.

As an Analytics Engineer, you'll own the data models and transformations that underpin credit decisioning, pricing, portfolio performance, and investor reporting for our UK and US businesses. You'll work closely with analysts, product teams, backend engineers, and business stakeholders to improve how data is structured, transformed, and consumed across the company. The role is fundamentally about building a strong analytical foundation: making it easier for teams to move from question to insight quickly, while maintaining high standards around data quality, scalability, and maintainability. You'll operate with a high degree of ownership, helping shape the modelling layer, improving how analysts work with data, and ensuring our warehouse remains a strategic asset for the business. What you'll be doing Owning and improving the data models that support lending decisions, pricing, portfolio analysis, and investor reporting. Driving the development of our dbt models and transformation layer, working with analysts and stakeholders to improve the speed and quality of insight generation. Helping define good modelling patterns, architecture, and implementation standards across the analytics engineering layer. Supporting and mentoring analysts at different technical levels, helping them build stronger engineering habits and become more effective with data. Acting as a bridge between analysts, backend engineers, product teams, and the data platform team to make sure data is generated, modelled, and used effectively. Leading triage and resolution of issues that affect the analytics pipeline or reduce trust in downstream datasets. Identifying opportunities to improve the efficiency, reliability, and cost-effectiveness of our transformation pipeline over time. Our modern data stack You'll work with a modern analytics stack centred around Snowflake, dbt, and Fivetran. What we're looking for We're looking for someone with strong analytics engineering fundamentals and the ability to apply them pragmatically in a fast moving environment. More specifically, we're looking for: Strong data modelling skills and a good understanding of how analytical datasets should be structured for reliability and usability. Strong experience with ELT pipelines and transformation at scale, ideally using dbt. Experience with Snowflake or another modern cloud data warehouse. Good judgement in balancing longer term platform improvements with day to day business needs. The ability to spot inefficiencies in existing data workflows and improve them independently. A collaborative working style and clear communication across technical and non-technical stakeholders. Comfort using AI tools effectively to move faster, improve quality, and strengthen day to day analytical and engineering workflows. An interest in helping analysts raise their technical bar through support, mentoring, and better shared patterns. Life at Lendable Winning team: the opportunity to scale up one of the world's most successful fintech companies Flexible working: flexible approach tailored to each role. Hybrid roles require three days in office weekly; fully remote roles include regular opportunities for in person connection through socials and off sites Socials & connection: opportunities and events to come together, socialise, and get to know each other beyond the office walls Health coverage: support for your physical and mental wellbeing, including private health cover Retirement & savings: long term financial wellbeing through retirement savings plans Employee referral programme: earn a competitive bonus when you refer successful new team members Office meals & snacks: enjoy a fully stocked kitchen, plus complimentary lunches prepared by in house chefs on in office days at select locations Sustainable commuting: cycle to work and electric vehicle salary sacrifice schemes available in select locations Please note: The availability and details of specific benefits vary by location and role. For more information, please speak to your Talent Partner.