What You'll Do

• Design and own end-to-end ETL/ELT pipelines, building flexible schemas that absorb data from a wide variety of sources.
• Carry out data modeling that powers real-time, customer-facing analytics and data products.
• Write and deploy high-performance transformations within the data warehouse using modern tooling.
• Partner with data consumers across the company to understand usage patterns and design intuitive data models.
• Own and configure the data platform (Databricks), working closely with product and engineering to ship data products the team actually uses.
• Shape the architecture of the data platform, warehouse, and infrastructure as much of it is still being built.
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What We're Looking For

• 3+ years of hands-on data engineering experience building and owning ETL/ELT pipelines end to end.
• Strong proficiency in SQL and data transformation workflows (dbt or equivalent).
• Hands-on experience with cloud data warehouses such as Databricks, Snowflake, or similar.
• Solid grounding in software engineering practices applied to data — version control, testing, and deployment patterns.
• Experience designing flexible schemas to ingest data from multiple heterogeneous sources and optimising query performance.
• Comfort preparing business aggregates and translating stakeholder requirements into data models.
• Background at a high-growth tech startup and an experimental, launch-fast-and-iterate mindset.
• Familiarity with Python, PySpark, Terraform, and AWS is a plus.