Aiviq Limited
Senior Data Engineer role within Aiviq Engineering Team. London, Hybrid / Full time Aiviq is a fintech company transforming how asset managers globally process, analyse, and leverage their data. Our solutions power critical business processes for leading financial institutions worldwide, helping them drive efficiency and growth. Position Overview We're seeking an accomplished Data Engineer to join our Data Centre of Excellence while working closely with our Engineering team on our sophisticated financial data management platform. This role combines the technical depth of enterprise data engineering with the fast-paced delivery demands of product development, requiring someone who is adept at translating business logic into code, can think architecturally while attending to implementation details. You'll be the bridge between our data architecture standards and practical product delivery, ensuring our financial data pipelines are robust, performant, and built on solid engineering principles. Core Responsibilities Design, build, and optimise data pipelines across Microsoft SQL Server and Azure Synapse Analytics environments Develop and maintain Spark SQL notebooks for complex data transformations and analysis Translate business logic and financial calculation requirements into clear, maintainable code Create data integrity checking scripts and validation frameworks in collaboration with QA teams Implement automated data quality checks and reconciliation processes Assist with the maintenance of a curated, anonymised dataset for system testing that covers all known scenarios and edge cases Analyse production datasets to identify anomalies, debug stored procedures and notebooks, and resolve data quality issues Demonstrate tenacity in investigating root causes, diving deep into complex problems until resolution is achieved Consult on database architecture decisions, balancing performance, scalability, and maintainability Optimise query performance and data processing workflows for large-scale financial datasets Design and implement solutions using Azure Data Factory, Delta Lake, and related technologies Think end-to-end about data flows while ensuring rigorous attention to implementation details Documentation & Process Create and maintain comprehensive documentation of database schemas, processes, and data flows Develop visual process models using tools such as Lucidchart, Visio, dbt, Azure Purview, or similar platforms Document data transformation logic and calculation methodologies for audit and compliance purposes Contribute to data governance standards and best practices across the organisation Act as first point of escalation for high-priority data issues in production environments Partner with test automation engineers to develop data-driven testing strategies and create data integrity checking scripts Collaborate across engineering teams using Azure DevOps for CI/CD pipeline development Support both Data CoE initiatives and product engineering priorities through effective stakeholder management Required Skills & Experience Technical Expertise Database Technologies: Strong proficiency in MS SQL Server and Azure Synapse Analytics Big Data Processing: Hands-on experience with PySpark, Spark SQL, and notebook-based development Cloud Platforms: Demonstrable experience with Azure ecosystem (Synapse, Data Factory, Delta Lake) Programming: Solid coding skills in SQL, Python, and/or C# Version Control: Experience with Git and Azure DevOps or similar CI/CD platforms Testing: Experience building out unit and integration test frameworks and processes to ensure pipelines and notebooks and other code artefacts are fully automation-tested Domain Knowledge Ideally a proven track record working with complex financial data and calculations Understanding of financial data structures, reconciliation processes, and audit requirements Experience handling temporal data, slowly changing dimensions, and historical data management Knowledge of data quality frameworks and validation methodologies Professional Capabilities Strong analytical and debugging skills for complex data scenarios across stored procedures and notebooks Relentless problem-solving approach - comfortable digging deep into technical issues and pursuing answers until problems are fully understood and resolved Experience with testing principles and data integrity validation Ability to consult on technical architecture while maintaining pragmatic focus Excellent documentation and process modelling capabilities Experience Level 5+ years in data engineering roles with increasing responsibility Track record of delivering production data systems at scale Experience working in matrix or cross-functional team structures Knowledge of Azure Purview or data cataloguing solutions Familiarity with Great Expectations or similar data quality frameworks Familiarity with data anonymisation, masking, and synthetic data generation techniques Experience with GraphQL APIs and modern application data layers Exposure to modern data visualisation tools (Power BI, Tableau) Experience with data build tool (dbt) or similar transformation frameworks Personal Attributes Collaborative mindset: Comfortable working across teams with different priorities and technical backgrounds Detail-oriented: Meticulous about data accuracy while maintaining delivery momentum Investigative nature: Thrives on troubleshooting complex issues and pursuing problems through multiple layers until fully resolved Intensely curious: Demonstrates deep curiosity about existing processes and systems, with a natural drive to investigate how things work and independently acquire new knowledge Pragmatic problem-solver: Balances architectural thinking with practical implementation Clear communicator: Articulates technical concepts to both specialist and generalist audiences Ownership mentality : Takes responsibility for production systems and follows through on commitments Cultural Fit Thrives in a matrix organization with multiple stakeholders Comfortable with ambiguity and competing priorities Passionate about engineering excellence and continuous improvement Values documentation and knowledge sharing Responds well under pressure during production incidents What We Offer Professional Development Exposure to enterprise-scale financial data systems handling complex calculations Opportunity to shape data engineering standards across the organisation Work with modern Azure cloud infrastructure and emerging technologies Collaborate with skilled engineers across test automation, software development, and data teams Matrix structure providing diverse learning opportunities from both CoE and product perspectives Hybrid working arrangement with flexibility Collaborative engineering culture valuing quality and craftsmanship Investment in tools, training, and professional growth Meaningful work on systems that impact financial data integrity and business decisions Aiviq is an equal opportunity employer and values diversity in our workforce. This role will be located in London although we are a hybrid team with frequent remote working and broader flexible working options available.
Senior Data Engineer role within Aiviq Engineering Team. London, Hybrid / Full time Aiviq is a fintech company transforming how asset managers globally process, analyse, and leverage their data. Our solutions power critical business processes for leading financial institutions worldwide, helping them drive efficiency and growth. Position Overview We're seeking an accomplished Data Engineer to join our Data Centre of Excellence while working closely with our Engineering team on our sophisticated financial data management platform. This role combines the technical depth of enterprise data engineering with the fast-paced delivery demands of product development, requiring someone who is adept at translating business logic into code, can think architecturally while attending to implementation details. You'll be the bridge between our data architecture standards and practical product delivery, ensuring our financial data pipelines are robust, performant, and built on solid engineering principles. Core Responsibilities Design, build, and optimise data pipelines across Microsoft SQL Server and Azure Synapse Analytics environments Develop and maintain Spark SQL notebooks for complex data transformations and analysis Translate business logic and financial calculation requirements into clear, maintainable code Create data integrity checking scripts and validation frameworks in collaboration with QA teams Implement automated data quality checks and reconciliation processes Assist with the maintenance of a curated, anonymised dataset for system testing that covers all known scenarios and edge cases Analyse production datasets to identify anomalies, debug stored procedures and notebooks, and resolve data quality issues Demonstrate tenacity in investigating root causes, diving deep into complex problems until resolution is achieved Consult on database architecture decisions, balancing performance, scalability, and maintainability Optimise query performance and data processing workflows for large-scale financial datasets Design and implement solutions using Azure Data Factory, Delta Lake, and related technologies Think end-to-end about data flows while ensuring rigorous attention to implementation details Documentation & Process Create and maintain comprehensive documentation of database schemas, processes, and data flows Develop visual process models using tools such as Lucidchart, Visio, dbt, Azure Purview, or similar platforms Document data transformation logic and calculation methodologies for audit and compliance purposes Contribute to data governance standards and best practices across the organisation Act as first point of escalation for high-priority data issues in production environments Partner with test automation engineers to develop data-driven testing strategies and create data integrity checking scripts Collaborate across engineering teams using Azure DevOps for CI/CD pipeline development Support both Data CoE initiatives and product engineering priorities through effective stakeholder management Required Skills & Experience Technical Expertise Database Technologies: Strong proficiency in MS SQL Server and Azure Synapse Analytics Big Data Processing: Hands-on experience with PySpark, Spark SQL, and notebook-based development Cloud Platforms: Demonstrable experience with Azure ecosystem (Synapse, Data Factory, Delta Lake) Programming: Solid coding skills in SQL, Python, and/or C# Version Control: Experience with Git and Azure DevOps or similar CI/CD platforms Testing: Experience building out unit and integration test frameworks and processes to ensure pipelines and notebooks and other code artefacts are fully automation-tested Domain Knowledge Ideally a proven track record working with complex financial data and calculations Understanding of financial data structures, reconciliation processes, and audit requirements Experience handling temporal data, slowly changing dimensions, and historical data management Knowledge of data quality frameworks and validation methodologies Professional Capabilities Strong analytical and debugging skills for complex data scenarios across stored procedures and notebooks Relentless problem-solving approach - comfortable digging deep into technical issues and pursuing answers until problems are fully understood and resolved Experience with testing principles and data integrity validation Ability to consult on technical architecture while maintaining pragmatic focus Excellent documentation and process modelling capabilities Experience Level 5+ years in data engineering roles with increasing responsibility Track record of delivering production data systems at scale Experience working in matrix or cross-functional team structures Knowledge of Azure Purview or data cataloguing solutions Familiarity with Great Expectations or similar data quality frameworks Familiarity with data anonymisation, masking, and synthetic data generation techniques Experience with GraphQL APIs and modern application data layers Exposure to modern data visualisation tools (Power BI, Tableau) Experience with data build tool (dbt) or similar transformation frameworks Personal Attributes Collaborative mindset: Comfortable working across teams with different priorities and technical backgrounds Detail-oriented: Meticulous about data accuracy while maintaining delivery momentum Investigative nature: Thrives on troubleshooting complex issues and pursuing problems through multiple layers until fully resolved Intensely curious: Demonstrates deep curiosity about existing processes and systems, with a natural drive to investigate how things work and independently acquire new knowledge Pragmatic problem-solver: Balances architectural thinking with practical implementation Clear communicator: Articulates technical concepts to both specialist and generalist audiences Ownership mentality : Takes responsibility for production systems and follows through on commitments Cultural Fit Thrives in a matrix organization with multiple stakeholders Comfortable with ambiguity and competing priorities Passionate about engineering excellence and continuous improvement Values documentation and knowledge sharing Responds well under pressure during production incidents What We Offer Professional Development Exposure to enterprise-scale financial data systems handling complex calculations Opportunity to shape data engineering standards across the organisation Work with modern Azure cloud infrastructure and emerging technologies Collaborate with skilled engineers across test automation, software development, and data teams Matrix structure providing diverse learning opportunities from both CoE and product perspectives Hybrid working arrangement with flexibility Collaborative engineering culture valuing quality and craftsmanship Investment in tools, training, and professional growth Meaningful work on systems that impact financial data integrity and business decisions Aiviq is an equal opportunity employer and values diversity in our workforce. This role will be located in London although we are a hybrid team with frequent remote working and broader flexible working options available.
Aiviq Limited
Senior Data Engineer role within Aiviq Engineering Team. London, Hybrid / Full time Aiviq is a fintech company transforming how asset managers globally process, analyse, and leverage their data. Our solutions power critical business processes for leading financial institutions worldwide, helping them drive efficiency and growth. Position Overview We're seeking an accomplished Data Engineer to join our Data Centre of Excellence while working closely with our Engineering team on our sophisticated financial data management platform. This role combines the technical depth of enterprise data engineering with the fast-paced delivery demands of product development, requiring someone who is adept at translating business logic into code, can think architecturally while attending to implementation details. You'll be the bridge between our data architecture standards and practical product delivery, ensuring our financial data pipelines are robust, performant, and built on solid engineering principles. Core Responsibilities Design, build, and optimise data pipelines across Microsoft SQL Server and Azure Synapse Analytics environments Develop and maintain Spark SQL notebooks for complex data transformations and analysis Translate business logic and financial calculation requirements into clear, maintainable code Create data integrity checking scripts and validation frameworks in collaboration with QA teams Implement automated data quality checks and reconciliation processes Assist with the maintenance of a curated, anonymised dataset for system testing that covers all known scenarios and edge cases Analyse production datasets to identify anomalies, debug stored procedures and notebooks, and resolve data quality issues Demonstrate tenacity in investigating root causes, diving deep into complex problems until resolution is achieved Consult on database architecture decisions, balancing performance, scalability, and maintainability Optimise query performance and data processing workflows for large-scale financial datasets Design and implement solutions using Azure Data Factory, Delta Lake, and related technologies Think end-to-end about data flows while ensuring rigorous attention to implementation details Documentation & Process Create and maintain comprehensive documentation of database schemas, processes, and data flows Develop visual process models using tools such as Lucidchart, Visio, dbt, Azure Purview, or similar platforms Document data transformation logic and calculation methodologies for audit and compliance purposes Contribute to data governance standards and best practices across the organisation Act as first point of escalation for high-priority data issues in production environments Partner with test automation engineers to develop data-driven testing strategies and create data integrity checking scripts Collaborate across engineering teams using Azure DevOps for CI/CD pipeline development Support both Data CoE initiatives and product engineering priorities through effective stakeholder management Required Skills & Experience Technical Expertise Database Technologies: Strong proficiency in MS SQL Server and Azure Synapse Analytics Big Data Processing: Hands-on experience with PySpark, Spark SQL, and notebook-based development Cloud Platforms: Demonstrable experience with Azure ecosystem (Synapse, Data Factory, Delta Lake) Programming: Solid coding skills in SQL, Python, and/or C# Version Control: Experience with Git and Azure DevOps or similar CI/CD platforms Testing: Experience building out unit and integration test frameworks and processes to ensure pipelines and notebooks and other code artefacts are fully automation-tested Domain Knowledge Ideally a proven track record working with complex financial data and calculations Understanding of financial data structures, reconciliation processes, and audit requirements Experience handling temporal data, slowly changing dimensions, and historical data management Knowledge of data quality frameworks and validation methodologies Professional Capabilities Strong analytical and debugging skills for complex data scenarios across stored procedures and notebooks Relentless problem-solving approach - comfortable digging deep into technical issues and pursuing answers until problems are fully understood and resolved Experience with testing principles and data integrity validation Ability to consult on technical architecture while maintaining pragmatic focus Excellent documentation and process modelling capabilities Experience Level 5+ years in data engineering roles with increasing responsibility Track record of delivering production data systems at scale Experience working in matrix or cross-functional team structures Knowledge of Azure Purview or data cataloguing solutions Familiarity with Great Expectations or similar data quality frameworks Familiarity with data anonymisation, masking, and synthetic data generation techniques Experience with GraphQL APIs and modern application data layers Exposure to modern data visualisation tools (Power BI, Tableau) Experience with data build tool (dbt) or similar transformation frameworks Personal Attributes Collaborative mindset: Comfortable working across teams with different priorities and technical backgrounds Detail-oriented: Meticulous about data accuracy while maintaining delivery momentum Investigative nature: Thrives on troubleshooting complex issues and pursuing problems through multiple layers until fully resolved Intensely curious: Demonstrates deep curiosity about existing processes and systems, with a natural drive to investigate how things work and independently acquire new knowledge Pragmatic problem-solver: Balances architectural thinking with practical implementation Clear communicator: Articulates technical concepts to both specialist and generalist audiences Ownership mentality : Takes responsibility for production systems and follows through on commitments Cultural Fit Thrives in a matrix organization with multiple stakeholders Comfortable with ambiguity and competing priorities Passionate about engineering excellence and continuous improvement Values documentation and knowledge sharing Responds well under pressure during production incidents What We Offer Professional Development Exposure to enterprise-scale financial data systems handling complex calculations Opportunity to shape data engineering standards across the organisation Work with modern Azure cloud infrastructure and emerging technologies Collaborate with skilled engineers across test automation, software development, and data teams Matrix structure providing diverse learning opportunities from both CoE and product perspectives Hybrid working arrangement with flexibility Collaborative engineering culture valuing quality and craftsmanship Investment in tools, training, and professional growth Meaningful work on systems that impact financial data integrity and business decisions Aiviq is an equal opportunity employer and values diversity in our workforce. This role will be located in London although we are a hybrid team with frequent remote working and broader flexible working options available.
Senior Data Engineer role within Aiviq Engineering Team. London, Hybrid / Full time Aiviq is a fintech company transforming how asset managers globally process, analyse, and leverage their data. Our solutions power critical business processes for leading financial institutions worldwide, helping them drive efficiency and growth. Position Overview We're seeking an accomplished Data Engineer to join our Data Centre of Excellence while working closely with our Engineering team on our sophisticated financial data management platform. This role combines the technical depth of enterprise data engineering with the fast-paced delivery demands of product development, requiring someone who is adept at translating business logic into code, can think architecturally while attending to implementation details. You'll be the bridge between our data architecture standards and practical product delivery, ensuring our financial data pipelines are robust, performant, and built on solid engineering principles. Core Responsibilities Design, build, and optimise data pipelines across Microsoft SQL Server and Azure Synapse Analytics environments Develop and maintain Spark SQL notebooks for complex data transformations and analysis Translate business logic and financial calculation requirements into clear, maintainable code Create data integrity checking scripts and validation frameworks in collaboration with QA teams Implement automated data quality checks and reconciliation processes Assist with the maintenance of a curated, anonymised dataset for system testing that covers all known scenarios and edge cases Analyse production datasets to identify anomalies, debug stored procedures and notebooks, and resolve data quality issues Demonstrate tenacity in investigating root causes, diving deep into complex problems until resolution is achieved Consult on database architecture decisions, balancing performance, scalability, and maintainability Optimise query performance and data processing workflows for large-scale financial datasets Design and implement solutions using Azure Data Factory, Delta Lake, and related technologies Think end-to-end about data flows while ensuring rigorous attention to implementation details Documentation & Process Create and maintain comprehensive documentation of database schemas, processes, and data flows Develop visual process models using tools such as Lucidchart, Visio, dbt, Azure Purview, or similar platforms Document data transformation logic and calculation methodologies for audit and compliance purposes Contribute to data governance standards and best practices across the organisation Act as first point of escalation for high-priority data issues in production environments Partner with test automation engineers to develop data-driven testing strategies and create data integrity checking scripts Collaborate across engineering teams using Azure DevOps for CI/CD pipeline development Support both Data CoE initiatives and product engineering priorities through effective stakeholder management Required Skills & Experience Technical Expertise Database Technologies: Strong proficiency in MS SQL Server and Azure Synapse Analytics Big Data Processing: Hands-on experience with PySpark, Spark SQL, and notebook-based development Cloud Platforms: Demonstrable experience with Azure ecosystem (Synapse, Data Factory, Delta Lake) Programming: Solid coding skills in SQL, Python, and/or C# Version Control: Experience with Git and Azure DevOps or similar CI/CD platforms Testing: Experience building out unit and integration test frameworks and processes to ensure pipelines and notebooks and other code artefacts are fully automation-tested Domain Knowledge Ideally a proven track record working with complex financial data and calculations Understanding of financial data structures, reconciliation processes, and audit requirements Experience handling temporal data, slowly changing dimensions, and historical data management Knowledge of data quality frameworks and validation methodologies Professional Capabilities Strong analytical and debugging skills for complex data scenarios across stored procedures and notebooks Relentless problem-solving approach - comfortable digging deep into technical issues and pursuing answers until problems are fully understood and resolved Experience with testing principles and data integrity validation Ability to consult on technical architecture while maintaining pragmatic focus Excellent documentation and process modelling capabilities Experience Level 5+ years in data engineering roles with increasing responsibility Track record of delivering production data systems at scale Experience working in matrix or cross-functional team structures Knowledge of Azure Purview or data cataloguing solutions Familiarity with Great Expectations or similar data quality frameworks Familiarity with data anonymisation, masking, and synthetic data generation techniques Experience with GraphQL APIs and modern application data layers Exposure to modern data visualisation tools (Power BI, Tableau) Experience with data build tool (dbt) or similar transformation frameworks Personal Attributes Collaborative mindset: Comfortable working across teams with different priorities and technical backgrounds Detail-oriented: Meticulous about data accuracy while maintaining delivery momentum Investigative nature: Thrives on troubleshooting complex issues and pursuing problems through multiple layers until fully resolved Intensely curious: Demonstrates deep curiosity about existing processes and systems, with a natural drive to investigate how things work and independently acquire new knowledge Pragmatic problem-solver: Balances architectural thinking with practical implementation Clear communicator: Articulates technical concepts to both specialist and generalist audiences Ownership mentality : Takes responsibility for production systems and follows through on commitments Cultural Fit Thrives in a matrix organization with multiple stakeholders Comfortable with ambiguity and competing priorities Passionate about engineering excellence and continuous improvement Values documentation and knowledge sharing Responds well under pressure during production incidents What We Offer Professional Development Exposure to enterprise-scale financial data systems handling complex calculations Opportunity to shape data engineering standards across the organisation Work with modern Azure cloud infrastructure and emerging technologies Collaborate with skilled engineers across test automation, software development, and data teams Matrix structure providing diverse learning opportunities from both CoE and product perspectives Hybrid working arrangement with flexibility Collaborative engineering culture valuing quality and craftsmanship Investment in tools, training, and professional growth Meaningful work on systems that impact financial data integrity and business decisions Aiviq is an equal opportunity employer and values diversity in our workforce. This role will be located in London although we are a hybrid team with frequent remote working and broader flexible working options available.