Want to know if this job is worth applying to?
Greater London, England, United Kingdom
Hybrid
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.
The Role
We're building the AI Assistant for commercial bankers. Relationship managers, treasury and credit teams use it to understand their business customers and act faster, and you'll be right in the middle of building it.
This is a hands-on job for people who like building and want to stay as an individual contributor. You'll design and ship the things the assistant depends on, and they'll still be yours once they're live. You'll also get a real say in where it goes next. That means making your case to engineers, product and the commercial team, and bringing them along with you.
What You'll Work On
The pipelines the assistant draws on, from raw source data to the numbers it quotes. You'll build them and keep them running.
The layers on top: metrics, definitions and the relationships between them, so a pile of financial data becomes something the assistant can reason over.
Testing and observability. We want what we ship to get more reliable, and we want the people around you to get better at it too.
You'll Probably Have
Built pipelines and production systems from scratch, writing the code yourself rather than mostly configuring managed services. If we ask about the hardest logic you've written, you'll have a story ready.
Good Python habits. Your tests are the kind someone else would be glad to inherit.
Real depth in a few of SQL, Spark, Databricks/Delta Lake, Dagster/Airflow/Temporal and dbt, and opinions about which ones annoyed you.
Time with CI/CD, Docker and cloud infrastructure. Even better if you set them up for a team.
An interest in the business behind the data. If the data says the request is wrong, you'll say so.
AI already built into how you work. You can show us where it saved you real time, or got you up to speed on something new.
Nice to have: some experience getting data ready for LLMs, like semantic layers, text-to-SQL or retrieval.
How We Work
We're a small team with plenty to do. Things move fast: we'd rather ship something useful and improve it than polish it in private. What you build is yours from start to finish.
There’s no return-to-office mandate, and you can work hybrid or fully remote. We trust you to be around when the team needs you and to deliver what you said you would. What we don’t want is anyone staying online late to prove a point.
