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Data Engineering Manager

Summary

Lead two data engineering teams for a SaaS energy-retail platform, improving pipelines, governance, and financial-reporting deadlines using Databricks, Spark, Python, and cloud infrastructure.

Our client is a SaaS platform for the energy retail sector, delivering cloud-native billing, customer management, and financial insight to energy suppliers across the UK and Europe. Following a recent acquisition, the business is expanding its AI product roadmap, with data engineering central to that ambition.


The role

You'll own two teams totalling around 12-13 people: one focused on new data source integration, governance frameworks, and testing/deployment maturity, and one on high-volume, time-critical processing with daily client-facing deadlines around financial reporting.

This is a hands-on technical leadership role, not a pure people-management position. You'll be close enough to the architecture and technical direction to hold a credible conversation with senior engineers, spot why a pipeline isn't working, and guide the team toward a fix, without needing to write the code yourself.


What we're looking for

  • A genuine track record of improving how a team delivers: process maturity, delivery metrics, continuous improvement, not just managing what's already working
  • Specific, evidenced examples of turning around underperforming teams or systems, not general leadership philosophy
  • Deep, hands-on experience with Databricks, Spark, Python, and SQL
  • Comfort with AWS, Azure, or GCP (AWS preferred, others fully considered)
  • Experience building or maturing data governance frameworks: access control, retention policies, data quality
  • Proven leadership of teams of 8+, ideally across more than one function
  • A background in regulated or sensitive data environments is a plus


What's on offer

  • Up to £110,000 base salary
  • Discretionary bonus of up to 25%
  • Fully remote, anywhere in the UK (occasional visits to a local office)
  • A genuinely central role in how the business's data function evolves, not a siloed back-office function

See also

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