You could be progressing your career in a senior, hands-on role at a well established consumer facing financial services company that help people to manage debt, as they go through a technical transformation and scale-up following recent investment.

As a Senior Data Engineer you will be responsible for designing, building and owning the new cloud (AWS) hosted data platform from the ground up. You'll collaborate with the Data Platform Lead, work closely with software engineering teams, the BI function and the Enterprise Architect to enable analytics, insight, AI and safe system transitions.

You'll own and evolve data ingestion pipelines, including incremental migration from a legacy monolith and Oracle-based systems, dual-running and reconciliation during platform transition (12-24 month initiative) and be responsible for the ongoing ingestion from systems of record (e.g. Sage, HR systems, HubSpot) and partners with the ultimate aim of establishing a hub-and-spoke, single source of truth data architecture that supports both transition and long term scale.

Location / WFH:

You'll be based in fantastic new offices in London that offer a wide range of facilities including wellness rooms and onsite gym for three days a week with flexibility to work from home twice a week.

About you:

• You are a Senior Data Engineer, with experience of designing and building data platforms end-to-end
• You have hands-on experience with cloud-native data infrastructure, ideally on AWS using IaC
• You have experience with Lakehouse and modern Data Warehouse patterns; AWS S3-based Lakehouse patterns, Athena / Redshift, Glue, EMR and related native services and Metabase for self-service analytics
• You have experience of designing and operating data ingestion pipelines at scale (batch and streaming)
• You have experience of supporting system migrations and dual-running, including reconciliation and trust building
• You have a strong understanding of data modelling for analytics
• You have experience of enabling self-service analytics platforms (e.g. Metabase or similar)
• You've had exposure to AI-adjacent data work, including: preparing data for RAG or ML use cases and supporting streaming or near-real-time analytics
• You're collaborative and pragmatic