Data Engineer
Data Engineer at a FinTech company in Manchester (hybrid, ~2 days/week in office), building a Databricks-native data platform: production ETL/ELT pipelines, high-volume ingestion, web scraping/automated collection, CI/CD, and Azure infrastructure supporting a growing Data Science function. Core stack: Databricks, Python, SQL, Spark.
- Compensation
- Not disclosed
- Location
- Manchester, United Kingdom
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Hands-on Databricks experience
- Hands-on web scraping experience
- Hands-on Python
- Hands-on SQL/Spark
- Hands-on ETL/ELT experience
- Understanding of databases and data warehousing
- Some exposure to platform, DevOps or systems engineering principles
- Technically curious, excited by AI, keeps up with new tools and approaches
Nice to have
- Playwright/Selenium or similar web scraping/automation tooling
- Databricks Asset Bundles, Lakeflow Jobs/Pipelines, Unity Catalog, Volumes and/or Lakebase
- Agentic or AI-enabled engineering workflows, LLM integrations or AI coding tools
- Terraform/Bicep
- Scala, PowerShell and YAML
- STEM background
Full description
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 Data Engineer to join the team and help build and develop 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 hands-on Databricks and web scraping experience are essential.
The role
You'll work within a cross-functional team, contributing to the move towards a Databricks-native environment and helping build reliable, scalable data and platform capabilities.
You'll be working across:
- Databricks, Python, SQL and Spark
- Web scraping and automated data collection
- 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. You'll be helping create the foundations that allow Data Science and automated workflows to operate effectively.
What we're looking for
Databricks and web scraping are the two big ones.
Alongside these, we're looking for hands-on experience with Python, SQL/Spark and ETL/ELT, an understanding of databases and data warehousing, and some exposure to platform, DevOps or systems engineering principles.
Just as important is mentality. They want someone who is technically curious, excited by what's happening across AI and constantly looking at what's next. Someone who experiments, keeps up with new tools and approaches, and naturally wants to stay ahead of the curve rather than waiting for new technology to become mainstream.
You don't need to be an expert in everything. They're looking for strong foundations, the right mindset and someone who wants to keep developing within a highly technical environment.
A STEM background is beneficial, but not essential.
Nice to have
- Playwright/Selenium or similar web scraping/automation tooling
- Databricks Asset Bundles, Lakeflow Jobs/Pipelines, Unity Catalog, Volumes and/or Lakebase
- Agentic or AI-enabled engineering workflows, LLM integrations or AI coding tools