Senior Data Engineer

About the role and team:

We are looking for a Senior Data Engineer to join our data team, taking ownership of the design, delivery, and reliability of production data pipelines built on platforms like Snowflake / Databricks, Python, and PySpark. You'll work with a high degree of autonomy, taking on the more complex and ambiguous data engineering challenges across client engagements, while mentoring junior engineers and helping to raise the bar on engineering practice. You'll collaborate closely with architects, product teams, and client stakeholders to translate data requirements into robust, maintainable solutions.

What you'll be doing:

• Take ownership of designing, building, and maintaining scalable, production-ready data pipelines using AWS data services (e.g., AWS Glue, EMR, S3, Redshift, Athena), Python, and PySpark, with minimal supervision
• Make sound technical decisions on pipeline architecture, data modelling, and tooling, weighing up trade-offs in performance, cost, and maintainability
• Mentor and support junior and mid-level data engineers, reviewing code and sharing knowledge to raise the standard of delivery across the team
• Own and continuously improve CI/CD pipelines for data engineering workflows, ensuring reliable, repeatable deployments
• Troubleshoot and resolve complex issues in live data pipelines, acting as a point of escalation for the wider team
• Partner with cross-functional teams and client stakeholders to understand data requirements and shape them into technical solutions

We'd love to talk to you if you:

• Have designed and operated production data pipelines on a modern cloud data platform, AWS (Glue, EMR, Redshift, S3/Lake Formation), Databricks, Snowflake or comparable, and can talk about which of those decisions would travel to a different one, and which wouldn't. 
• Have owned orchestration in production, dependency design, retries, SLAs, and the observability that tells you a pipeline failed before the client does.
• Have landed data from awkward sources: APIs, CDC off operational databases, files that change shape without telling you.
• Have deep SQL skills and hands-on experience with data modelling (e.g., Kimball, medallion/lakehouse patterns, or domain-driven modelling) — this is the load-bearing, provider-agnostic skill underneath almost all of our pipeline work
• Have strong Python and PySpark for batch processing, with production-grade, well-tested code. The bulk of this work is batch and ELT, but you'll need to have experienced event-driven or micro-batch pipelines and know where the design assumptions stop holding. )
• Have experience owning the delivery of data engineering work with limited oversight, including CI/CD management and support
• Have experience mentoring or supporting the development of less experienced engineers
• Are comfortable working directly with stakeholders to shape technical solutions from ambiguous requirements, presenting your work, estimation and discussing tradeoffs and risks.