Senior Data Engineer

• Playing a key role in shaping the foundations of a Databricks-based lakehouse platform - designing how the catalogue is structured, defining core dimensions/facts, and ensuring the platform is discoverable and useful across the business.
• Writing clean, performant Python, SQL, and working confidently with Spark/PySpark.
• Integrating third-party tools, connectors, and SaaS data sources into a cohesive data ecosystem.
• Owning software components end-to-end: from idea, to build, to production (ensuring reliability and maintainability).
• Championing continuous improvement and modern engineering practices.
• Working closely with cross-functional stakeholders to turn real-world problems into elegant data solutions.
• Producing clear, concise technical documentation.
• Adapting within a fast-evolving environment and contributing across the data remit wherever needed.

Do you have experience in Spark?, * Have hands-on experience building Databricks lakehouse architectures and are excited by shaping foundational data infrastructure.

• Understand how to engineer data platforms for trust, scalability, and discoverability, not just produce pipelines.
• Are confident with Databricks, AWS, and the modern data stack.
• Enjoy fast-paced, iterative delivery and creating user-friendly, value-driven outcomes.
• Collaborate naturally, share ideas openly, and learn from those around you.
• Are adaptable, curious, and motivated by continuous improvement and learning.
• Bring strong experience in data engineering, particularly in greenfield or scaling environments (or equivalent).
• Embrace "data as a product" thinking - ensuring datasets have clear purpose, documentation, quality checks, version control, and measurable value.
• Think like a seasoned engineer: Git, CI, modular code, automated tests, alerting, and clean architecture are second nature.
• Are excited to establish foundational patterns that others will follow.