Senior Data Engineer
Summary
Senior Data Engineer at a disruptive FinTech in Manchester (hybrid, ~2 days/week in office), building and evolving a Databricks-native data platform: high-volume ingestion, production ETL/ELT pipelines, CI/CD, and Azure platform reliability to support a growing Data Science and ML function.
Manchester | Hybrid, ideally 2 days per week in office
We’re working with a truly disruptive FinTech that is continuing to invest heavily in its data and technology capability.
With a growing Data Science function and around 3–5TB of new data being processed each week, they’re looking for a Senior Data Engineer to help build and evolve the platform behind it.
This isn’t necessarily a traditional Data Engineering profile. They’re open to people from Data Engineering, Platform, DevOps or Systems Engineering backgrounds, but strong data foundations and hands‑on Databricks experience are key.
The roleYou’ll work within a cross-functional team, helping continue the move towards a Databricks‑native environment and building reliable, scalable data and platform capabilities.
You’ll be working across:- Databricks, Python, SQL and Spark
- High-volume data ingestion and transformation
- Production ETL/ELT pipelines
- APIs, SFTP/FTPS and automated data retrieval
- CI/CD, automated testing and deployment
- Platform reliability, monitoring and troubleshooting
- Azure infrastructure, security and governance
- Data Science, ML and increasingly agentic workflows
The focus is production engineering rather than building AI models, creating the foundations that allow Data Science and automated workflows to operate effectively.
What we’re looking forDatabricks is the big one. Alongside that, we’re looking for strong Python, SQL/Spark and ETL/ELT experience, good knowledge of databases and data warehousing, including dimensional/Kimball principles, plus an understanding of platform, DevOps and systems engineering.
They also value genuine technical curiosity. If you keep up with new technology, experiment with AI/deep learning or have side projects outside your day job, they’ll want to hear about them.
A STEM background is beneficial, but not essential.
Nice to have- Databricks Asset Bundles, Lakeflow Jobs/Pipelines, Unity Catalog, Volumes and/or Lakebase
- Agentic or AI-enabled engineering workflows, LLM integrations or AI coding tools
- Playwright/Selenium
- Terraform/Bicep
- Scala, PowerShell and YAML
You’ll join a highly technical, collaborative team working closely with a growing Data Science function. Ideally you’ll spend around two days per week in the Manchester office, but it’s an output-driven environment with plenty of autonomy.
Diversity & InclusionWe welcome applications from people of all backgrounds and experiences. If the role interests you but you don’t tick every box, we’d still encourage you to apply. Different experiences, perspectives and routes into technology are valued.
What they ask for
Required
- Strong hands-on Databricks experience
- Strong Python skills
- SQL and Spark experience
- ETL/ELT pipeline experience
- Good knowledge of databases and data warehousing, including dimensional/Kimball principles
- Understanding of platform, DevOps and systems engineering
- Open to Data Engineering, Platform, DevOps or Systems Engineering backgrounds with strong data foundations
Preferred
- STEM background
- Genuine technical curiosity: keeping up with new tech, experimenting with AI/deep learning, or side projects
- Databricks Asset Bundles, Lakeflow Jobs/Pipelines, Unity Catalog, Volumes and/or Lakebase
- Agentic or AI-enabled engineering workflows, LLM integrations or AI coding tools
- Playwright/Selenium
- Terraform/Bicep
- Scala, PowerShell and YAML
