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Job Description & How to Apply Below
We are looking for a senior, hands‑on Data Engineer who can build and scale a modern data platform while also providing technical leadership to the engineering team. The candidate must still be close to the technology and directly involved in implementation. We are not looking for a profile that is mainly focused on architecture, advisory work or people management.
Core requirements- 8–10+ years of experience in Data Engineering, ETL development or data-platform engineering, including experience leading or mentoring engineers.
- Strong recent and demonstrable Databricks implementation experience
. The candidate should have personally built or implemented solutions on Databricks, rather than only defining the architecture or managing delivery. - Strong hands‑on capability in Python, SQL and Spark
. - Proven experience designing and building scalable ETL/ELT and ingestion pipelines in complex, multi‑source environments.
- Experience creating structured data layers, data warehouses, data marts and lakehouse solutions for enterprise reporting, analytics and AI use cases.
- Experience with metadata‑driven ingestion frameworks, reusable engineering patterns and platform administration would be highly relevant.
- Strong understanding of production engineering practices, including pipeline monitoring, automated testing, error handling, data freshness, reliability and performance.
- Practical knowledge of data governance, catalogues, metadata, lineage, data quality, MDM, role‑based access control and security.
- Experience with batch and real‑time processing. Exposure to Databricks Structured Streaming or Delta Live Tables would be useful.
- Hands‑on experience with a major cloud platform, preferably Azure, although AWS or GCP experience may also be considered.
- Ability to set engineering standards, review code, mentor engineers and lead delivery without moving away from hands‑on technical work.
- Clear and concise communication, with the ability to work with architects, analytics teams, AI teams and business stakeholders.
- Databricks, Azure or other relevant cloud/data‑engineering certifications would be beneficial.
- Exposure to APIs and data services used by enterprise applications, analytics platforms, ML pipelines or agentic systems would be useful.
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