Senior Data Engineer

Who you are

• Significant experience building and operating AWS data platforms, ideally with a lakehouse or warehouse in production
• Strong hands-on Spark or Glue, Apache Iceberg or equivalent table format, and a warehouse such as Redshift
• Strong Python and SQL with a record of tested, maintainable pipeline code
• CDC and replication (DMS, Zero-ETL) and integrating SaaS/ERP data via APIs
• Solid data modelling and experience serving BI and analytics consumers
• Infrastructure as code and CI/CD, ideally Terraform with CircleCI or GitHub Actions
• Good grasp of data protection and governance: classification, least privilege, PII minimisation
• Daily use of AI coding agents; interest or experience in exposing data to AI tools (e.g. MCP, semantic
• Layers) with governance
• Clear communication with technical and business audiences; experience supporting or mentoring other engineers
• Experience leading platform architecture or a small data workstream end to end; ready to grow into broader ownership
• Amazon Quick (QuickSight) experience; ClickHouse or other columnar engines; Lake Formation, S3 Tables and Glue 5
• Power BI is nice to have but not part of the day-to-day role; energy, IoT or time-series data
• Building self-service or low-code data tooling for business users; TypeScript and serverless experience
• Personal attributes: hands-on with the credibility to set standards; pragmatic about cost and complexity; curious about the business; calm, methodical and data-driven; advocates for privacy, quality and sustainability

What the job involves

• A Senior Data Engineer to lead the build-out and adoption of our AWS data lake and help define how the business gets value from it
• You will lead the design and delivery of our medallion lakehouse on Apache Iceberg, the pipelines that feed it and the datasets our BI users and AI tools consume
• You will build the framework that lets business users prototype pipelines and automations safely, with guardrails on quality, cost and data protection.
• A hands-on senior role reporting to the Head of Software & Cybersecurity and working alongside the DevOps team: you set the data engineering standards, support the DevOps engineers who serve data today, use AI tools daily and make the platform something AI agents can safely consume, with scope to take on more ownership as the data function grows
• Lead the data lake architecture and roadmap, agreed with the Head of Software: bronze/silver/gold on S3 Tables with Apache Iceberg, Glue Catalog, Lake Formation
• Design ingestion per source: Zero-ETL (Aurora/RDS to Redshift), DMS, APIs (HubSpot, ERP) and device telemetry
• Recommend how Redshift, Iceberg and ClickHouse each fit our analytical workloads, cost-aware
• Set modelling standards: silver stays use-case agnostic; every gold metric is traceable to source
• Build and maintain Glue/PySpark jobs, Lambdas and orchestration with quality gates and tested code
• Own pipeline performance, reliability and cost: partitioning, compaction, tuning, monitoring, alerts
• Data protection by design: classify sources, minimise PII at source, mask lower environments; keep lineage docs current
• Build and run the framework for business users to prototype pipelines and automations safely, with clear promotion paths to production
• Partner with finance, sales, service and product to turn questions into reusable, well-modelled gold datasets; measure time from question to trusted answer
• Make the platform AI-ready: governed, well-described datasets exposed to AI agents (e.g. via MCP) with least-privilege access and audit trails; help define the guardrails
• Use AI coding agents daily to build, test and document pipelines; set the patterns the team follows
• Set data engineering standards: Terraform, CircleCI CI/CD, code review, testing and documentation
• Support and mentor the DevOps engineers delivering data extracts and pipelines; pair and review designs
• Work with the backend team (TypeScript, Lambda, DynamoDB, Postgres) so application data is consumable by the platform