Senior Data Engineer

Own the foundational data model. Design how our core entities look and are structured in the warehouse. Make the calls on grain, slowly changing dimensions, snapshot strategy, and the conventions our staging and mart layers are built on.

Set the standards . Define how we do testing, documentation, orchestration, and alerting across all our data infrastructure and bring our existing warehouses up to that bar.

Build and operate the ingestion layer. Design and maintain pipelines that move data reliably from internal and external sources into the warehouse, using Fivetran, dbt and Python where each fits best. Implement the data quality checks, alerting, and troubleshooting tools that let us catch issues before stakeholders do.

Architect for governance. Build role-based access, and audit controls into the foundation so that governance scales with the platform instead of being retrofitted later.

Help shape the platform decision. We're evaluating our long-term data platform and whether to consolidate on what we have or move to something new. You'll partner with our Head of Data and Analytics on the evaluation and own the migration end-to-end once the decision is made.

5+ years of experience in Data Engineering, including time as the most senior data engineer on a team or workstream.

Production experience with a modern data toolset including dbt, a cloud warehouse or lakehouse, and modern orchestration and ingestion tooling.

Strong dimensional modeling fundamentals — you can defend a choice between Kimball, Inmon, or a hybrid based on the problem in front of you, and you've designed type-1/type-2 SCDs in production.

Track record of setting standards on documentation, testing and alerting on a team that didn't have them.

You've built data infrastructure under real regulatory constraints at an RIA, broker-dealer, bank or anywhere SOC 2 / Reg S-P / shaped what you built and how.

You leverage AI as a force multiplier in your own work. You've developed real instincts for when to lean on it, when to verify, and where it actually accelerates pipeline development, modeling, and debugging.

You've done a warehouse consolidation or platform migration end-to-end where you made the architectural calls and lived with the consequences and not just executed someone else’s plan