Senior Data Engineer

Role overview A hands-on engineering role on a lean platform team building a new AWS-based data mesh for an enterprise client transitioning from centrally owned data to federated data ownership. The work combines building first-wave data products, onboarding data from legacy warehouses, and acting as an enablement partner for product engineering teams adopting data ownership.

Responsibilities - Design and build consumable data products on the AWS platform, including schemas, documentation, and clear ownership, then hand them over to product teams - Agree data contracts, map domain ownership, and help teams understand how their data fits the wider landscape - Onboard bronze and silver layer data from existing warehouses using internal agentic tooling - Build new tooling that accelerates adoption of the data platform - Develop streaming and batch pipelines using Flink, SQL, Python, Kafka, Iceberg, and DynamoDB on AWS - Help engineering teams debug pipeline and batch job issues - Promote engineering best practices through pairing, reviews, and hands-on guidance rather than top-down mandates - Drive technical initiatives end to end at Lead Engineer level

Requirements - Solid data engineering background on large-scale data platforms with big data workloads - Hands-on experience with stream processing, event-driven architecture, and schema management - Strong streaming experience combined with practical batch data engineering - Working AWS knowledge with the ability to stand up streaming pipelines and batch jobs - Practical experience with Kafka, Iceberg, DynamoDB, and core AWS services - Exposure to Apache Flink using SQL or PyFlink, productive within weeks - Lead-level experience setting technical direction and owning outcomes rather than individual tickets - Strong stakeholder management and ability to explain the value of data ownership - Practical use of AI-powered assistants such as Claude Code, GitHub Copilot, or Cursor to improve delivery quality and productivity - Fluent written and spoken English

Nice to have - AI-assisted engineering and agentic tooling applied to data onboarding, tooling, and pipeline development - Exposure to data mesh concepts including data products, data contracts, domain ownership, and federated governance - Kubernetes awareness sufficient to understand how the platform runs - Experience in data platform enablement or internal developer experience roles - Background in high-volume operational or transactional environments where volume and latency both matter - Structured use of generative AI within the SDLC

Benefits and work setup - Remote work based in the European Union; a valid EU work permit is required - Recruitment process: CV review, HR call, interview, client interview, decision