Full time
Information Technology
Telecommunications
Job Description
Requirements
Experience building and deploying AI/ML-powered applications
A solid understanding of responsible AI principles and how they apply to data infrastructure and product development
5+ years of commercial experience as a data engineer across multiple data products and pipelines
Advanced knowledge of GCP as a data warehouse
Advanced SQL skills with the ability to design and optimise complex queries at scale
Deep familiarity with modern analytics and data architectures, including data warehousing best practices
Experience with scalable backend systems and cloud platforms, with comfort across the full data engineering lifecycle - ingestion, cleaning, transformation and integration
Experience with dbt, Fivetran and Snowflake
Python proficiency
Previous experience in health, wellness, pharmaceutical or digital/e-commerce sectors. nice to have
Customer-focused: you are genuinely excited about building products that drive positive change at scale
Self-starter: you use your own initiative, anticipate problems before they arise and take full ownership from raw ingestion to final delivery
Pragmatic problem-solver: you take genuine pleasure in finding the right solution and making sensible trade-offs under real-world constraints
Growth mindset: you thrive in fast-paced scale-up environments and are energised, not unsettled, by ambiguity and pace
What the job involves
You will join our Data team as owner of our data infrastructure with an opportunity to architect and own the data pipelines, platforms and practices that will power our Data Platform
A key part of this role is improving and defining how stakeholders can use AI to self-serve their own analysis
You'll sit at the intersection of data engineering and applied AI, working closely with analytics engineers and analysts
You will define the standards and systems that allow the data team and stakeholders to self-serve analysis using AI as a base- ensuring the data foundation is reliable, scalable and ready to fuel our AI and LLM capabilities
Build and maintain scalable data pipelines to ingest, transform and integrate data from multiple sources, defining best practice along the way
Implement robust monitoring for data ingestion jobs and take swift corrective action when issues arise
Work with business stakeholders to translate requirements into efficient pipeline architectures, database structures and optimised data models
Partner with analytics engineers to ensure clean, accessible data is available to analysts across the organisation
Champion responsible AI principles in the design and deployment of data systems that underpin our LLM and RAG implementations
Lead on development standards, version control, quality control, deployment and change management processes
Monitor, measure and maintain high data quality standards across all data products and pipelines