Own the data warehouse (currently Snowflake-shaped, modern stack), modelling, and transformation layer (dbt)
Refactor and harden the admin API data flows — currently a major source of technical debt
Build and maintain production pipelines on AWS — Python + SQL, with proper observability and testing
Partner with backend engineers to expose clean, reliable data at source rather than retrofitting downstream
Work as a bridge between Engineering and Product to expose the right data for customer outcomes
Contribute to backend feature work where data and product cross over (telemetry, instrumentation, data-aware features)
Sit inside the engineering team — you are an engineer who specialises in data, not a separate analytics function

5+ years in a data engineering role, ideally including time at a B2B SaaS or product startup
Strong Python and SQL — these are languages, not tools; if either is weak, the rest doesn't hold up
Hands-on experience deploying and operating data pipelines in production on AWS
Experience with a modern warehouse (Snowflake, BigQuery, Databricks, Redshift) — specific tech is not a hard requirement, but depth in at least one is
Hands-on with DBT or equivalent transformation framework
Backend engineering fluency — you can read and contribute to backend code so you can fix data issues at source
Comfortable in a small, fast-moving team where you own outcomes end-to-end.

Senior Data Engineer

Key skills