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.

What you'll do

• 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.

Who you are

AI & LLM expertise

• 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.

Data engineering foundation

• 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

How you work

• 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.

Department

Data

Locations

London

Remote status

Hybrid