Location: Manchester (2 days onsite per week)
The Opportunity
We are seeking a Data Engineer to join a scaling engineering team responsible for managing one of the UK’s most significant high-volume data platforms. You will be working on a platform that processes high volumes of records, transforming raw digital interaction data into actionable insights for a global client base.
This is a pure engineering role focused on building and optimizing production-grade pipelines at scale. You wont just be moving data; you will be solving complex architectural problems across both real-time and batch processing environments.
The Role
* Architect & Build: Develop and maintain robust ETL pipelines using Python, Spark, and SQL.
* Cloud Infrastructure: Leverage the AWS ecosystem (including EMR, Athena, and Lambda) to ensure high availability and performance.
* Optimization: Improve the scalability and reliability of a platform handling massive, complex datasets.
* Collaboration: Partner with product teams to translate high-level requirements into technical data solutions.
* Quality: Contribute to a culture of clean code, automated testing, and continuous CI/CD improvement.
* Operations: Participate in a compensated on-call rotation to support mission-critical production systems.
What We’re Looking For
* Data Expertise: Strong professional experience in Python and SQL within a data engineering context.
* Big Data Tools: Hands‑on experience with Apache Spark (PySpark or Scala) for large-scale processing.
* Cloud Native: Proven experience building and deploying data solutions on AWS (or a similar cloud provider).
* Problem Solvers: A track record of debugging and improving complex production systems at scale.
* Engineering Standards: A solid understanding of software engineering best practices, including CI/CD and unit testing.
Why Apply?
* Technical Challenge: Work on true "web‑scale" problems that very few Manchester‑based firms offer.
* Modern Stack: Join a business that heavily invests in the latest data tooling and engineering best practices.
* Career Growth: Clear progression paths into Senior or Specialist Engineer tracks within a growing team.
* Flexibility: A mature hybrid working model with a focus on output rather than hours at a desk.
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