Senior Data Engineer

About Codat

Codat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.

Founded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.

We're looking for a Senior Data Engineer to join our Data and Insights team. You'll be hands-on every day, writing production code, building and maintaining data pipelines, and shipping features that turn raw data into intelligence our clients can act on. You'll work across the full project lifecycle, from understanding the problem through to delivery, and you'll care as much about code quality and operational reliability as you do about getting things shipped.

This is also a technical leadership role. As a senior member of the team, you'll set and lead the technical direction of our Insights platform. This is a visible position within engineering and across the wider business, so you'll explain your thinking clearly, share the reasoning behind it, and bring people with you. You'll do all of this while staying close to the code: it'll suit you if you want to keep building hands-on, rather than move into pure architecture or people management in the near term.

• Write production code every day, most likely in Python, building and maintaining the data pipelines that power our Insights products.

• Own the full lifecycle of your projects, from understanding the data domain through to pragmatic design, shipping, and keeping things running reliably in production.

• Set and lead the technical direction of the Insights platform, and communicate it openly across engineering and the wider business, so product and commercial colleagues understand the choices you are making and why.

• Help raise engineering standards across the team and improve technical quality through strong engineering practice, including testing, observability, data quality checks, and clean, maintainable code.

• Make AI your default way of working, and find opportunities to apply it across our products and pipelines where it delivers real value, from research and prototyping through to more operational uses such as agents that help diagnose and fix pipeline issues.

• Help lay the foundations for our emerging MCP and semantic layer, so our data becomes something both people and AI systems can query and reason over.

• Strong software engineering fundamentals: you write well-tested, production-ready Python and care about maintainability, observability, and operational excellence.

• A track record of building data pipelines and production systems from the ground up, rather than mainly configuring managed services or wiring off-the-shelf tools together. You can describe complex logic you've written and the engineering problems you had to solve.

• Solid experience with modern data engineering tools and patterns, with real depth in several of SQL, Spark, Databricks/Delta Lake, orchestration tools (Dagster, Airflow, Temporal), and dbt.

• Comfort with modern deployment practices: CI/CD, containerisation (Docker), and cloud-based infrastructure. It's a bonus if you've shaped these for a team, not only worked within them.

• A product mindset: you want to understand the business domain and use that understanding to shape what gets built, not only how. You're comfortable pushing back or proposing a different approach when your read of the data and the domain calls for it.

• Strong communication skills: you can explain and build support for your ideas with peers, managers, and non-technical stakeholders, and you're comfortable holding a visible technical position and bringing people with you.

• AI as a default part of how you work, with evidence of real efficiency gains and creative use beyond code generation, such as research, building domain knowledge, or prototyping.

• Nice to have: exposure to the building blocks of AI-ready data, such as semantic layers, ontologies, or text-to-SQL, plus any experience applying AI operationally within data platforms or pipelines. This won't be your main focus, but it will help as our platform grows to support an MCP.