Senior Data Engineer
CodatUniversal API company
London, United KingdomSenior
J.P. Morgan
Tiger Global
PayPal Ventures
Canapi Ventures
Shopify
Plaid
Data & AI
About the role
TL;DR
Senior Data Engineer to build and maintain data pipelines and lead technical direction for the Insights platform.
- •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.
- •Key Responsibilities 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.
- •Help raise engineering standards across the team and improve technical quality through strong engineering practice.
- •Make AI your default way of working, and find opportunities to apply it across our products and pipelines.
- •Requirements 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.
- •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.
- •A product mindset: you want to understand the business domain and use that understanding to shape what gets built.
- •Strong communication skills: you can explain and build support for your ideas with peers, managers, and non-technical stakeholders.
- •AI as a default part of how you work, with evidence of real efficiency gains and creative use beyond code generation.
Required skills
PythonSQLSparkDatabricksDagsterAirflowdbtDockerCI/CD
Nice-to-have skills
LLMs
Domain expertise
fintech
Tech stack
PythonSQLSparkDatabricksDagsterAirflowdbtDockerCI/CD