Senior Data Engineer

We are seeking a Senior Data Engineer to be the most experienced engineer on our data team. This is a hands-on role by design. You will pair with the team daily, set the standards the data function works to, and take the lead on the architecture decisions that shape what we build next.

The remit is broad. You will own the target design for the warehouse and the curated layer that sits on top of it, define the semantic layer so that every business metric carries one agreed definition, and establish the standards for naming, lineage, and data quality that the whole function works to. All of it serves the same end. A practice should be able to see the patient journey clearly enough to improve it, see where its time and capacity actually go, and know which of its decisions grew the business. You will also take migration from something skilled people do carefully to something the platform does repeatably.

Our warehouse is Amazon Redshift running a medallion model, fed from PostgreSQL by Python pipelines and from marketing sources by Fivetran, with Power BI on top. You will own the recommendation on where that architecture goes next, and then you will build it.

We also build with AI as a default rather than as an experiment. Coding agents draft quickly here, so the skill we hire for and level on is knowing what to build and recognizing what is wrong with a draft. Our data team already works with Claude Code connected to the warehouse every day.

• 8+ years in data engineering, including end to end ownership of a warehouse or lakehouse architecture, from ingestion through to the curated layer that people actually query.
• Deep SQL and Python, with production experience on a modern cloud warehouse. Redshift is what we run today, and Snowflake, BigQuery, or Databricks experience transfers.
• Hands-on experience building a semantic or metrics layer yourself, using dbt or an equivalent tool.
• Production experience with orchestration and infrastructure as code on AWS (e.g., Airflow and Terraform).
• Experience designing and maintaining dimensional or Data Vault models in production..
• Data quality testing and observability built into the pipeline, using dbt tests, Great Expectations, or an equivalent, alongside daily use of coding agents and AI tooling in your own work.
• AI applied inside a data platform, such as LLM classification of unstructured records, entity matching, anomaly detection, or natural language querying over a semantic layer, with an evaluation set that told you how well it worked.
• Experience with regulated data, such as healthcare or financial data.