Data Engineer (Spark/Kubernetes) (Financial Services)

We are seeking a Data Engineer to support the replacement of a Legacy ETL tool with a modern Apache Spark based data platform. This is a hands-on engineering role focused on building and supporting Spark jobs, with an emphasis on performance, reliability, and scalability.

The role is focused on building nonperformance Apache Spark jobs, with a strong emphasis on performance optimisation. You shall be running Spark workloads in containerised environments using Kubernetes and programming skill set in Python/Scala or Java is also a required skill set.

The role sits within a small Agile delivery team of four engineers (two onshore and two in Shenzhen), working closely with a Senior Data Engineer. You will be responsible for development work, sprint delivery, demos, documentation, and stakeholder engagement. This position suits a mid-level engineer with strong Spark development experience rather than design, infrastructure, or management responsibilities.

• Strong hands-on experience with Apache Spark - Writing and tuning Spark jobs/PySpark development experience.
• Strong experience working in with containerised environments using Kubernetes.
• Experience with programming in Python or Scala
• Exposure to Big Data technologies and distributed data processing
• Have some experience using Java/Java Spring boot for development.
• Experienced in an Ops way of working, not pure development only - you know how to deploy solutions.
• Experience with OpenShift would be highly desirable!
• Experience working in an Agile way of working (Scrum, sprints, demos)
• Financial services or professional services experience required.

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