Senior Data Engineer

Pipeline Engineering: Build and operate robust ingestion pipelines (both batch and streaming) from databases, event streams, and third-party APIs.

Storage & Performance: Own storage architecture across the lake, lakehouse, and warehouse, optimizing for partitioning, cost efficiency, and query speed.

Reliability & Observability: Keep pipelines observable and restartable by implementing orchestration, automated testing, alerting, and ensuring clean recovery when something breaks at 08:00.

Engineering Best Practices: Work like a true software engineer by implementing infrastructure as code, CI/CD for data workloads, code reviews, and strict version control.

Data Contracts & Governance: Agree on data contracts with data-producing teams to keep schema drift under control, while managing access, retention, and lineage.

Lasting Documentation: Produce clear and comprehensive documentation that ensures your work outlives the engagement.

Has Proven Production Experience: You have built and ran data pipelines in production—and, more importantly, you can articulate what you learned when they broke.

Is an AI-Augmented Engineer: You seamlessly integrate AI tooling into your daily workflow, but you maintain the strict review discipline to verify what it generates before it ships.

Is a Confident Communicator: You possess fluent English and are completely comfortable working directly with customer teams.

Has a Cost Engineering Mindset (Valued): You know how to make a pipeline cheaper and can quantify the savings.

Understands Compliance (Valued): You have exposure to regulated environments, including GDPR obligations, access control, and auditable lineage.