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Codat is an advisory intelligence platform for commercial banking, backed by JPMorgan, PayPal, Amex, Plaid, and Shopify. The company has powered over 350,000 connections to business financial systems and helps banking teams deepen relationships, grow revenue, and simplify operations through rich data and forward-looking insights.
You'll join the Data and Insights team as a Senior Data Engineer, working hands-on every day writing production code and building data pipelines that transform raw data into actionable intelligence for clients. This is a technical leadership role where you'll set and lead the technical direction of the Insights platform while remaining close to the code—ideal if you want to keep building rather than move into pure architecture or people management.
Key responsibilities include: writing production Python code daily to build and maintain data pipelines; owning the full project lifecycle from problem understanding through design, shipping, and production reliability; setting and communicating the technical direction of the Insights platform across engineering and the business; raising engineering standards through strong practices like testing, observability, and data quality checks; making AI a default part of your workflow to improve products and pipelines; and helping establish foundations for the emerging MCP and semantic layer so data can be queried by both people and AI systems.
You bring strong software engineering fundamentals with well-tested, production-ready Python and a track record of building data pipelines and production systems from the ground up. You have solid experience with modern data engineering tools (SQL, Spark, Databricks/Delta Lake, Dagster, Airflow, Temporal, dbt) and modern deployment practices (CI/CD, Docker, cloud infrastructure). You have a product mindset, understanding business domains and shaping what gets built, not just how. You communicate clearly with peers, managers, and non-technical stakeholders, comfortable in a visible technical position. You use AI as a default part of your workflow with evidence of real efficiency gains beyond code generation. Nice-to-have experience includes semantic layers, ontologies, text-to-SQL, and operational AI within data platforms.
