Data Engineer
Summary
Data Engineer building and maintaining scalable Databricks data pipelines, automating processes with Python, and creating performance dashboards in Streamlit/HTMX for finance and asset management teams.
Help us make a big green dent in the universe.
Key Responsibilities
- Design, build, and maintain scalable data pipelines in Databricks, ensuring data is reliable, timely, and well-documented.
- Own and evolve our Databricks models - define schemas, enforce testing, and keep the warehouse clean.
- Automate manual data processes using Python, replacing spreadsheets and ad-hoc workflows with robust, repeatable jobs.
- Build performance dashboards and reports in Streamlit and HTMX (FastAPI) that Finance, Asset Management, and Fund Management actually use.
- Integrate internal and third-party systems via REST APIs, improving data accessibility across the business.
- Champion data quality - implement validation, monitoring, and governance practices that give stakeholders confidence in the numbers.
- Collaborate directly with non-technical teams to understand their problems and translate them into data solutions.
Requirements
- Strong SQL and Python skills, used daily for pipeline development, data modelling, and automation.
- Experience with modern data stack tooling: a cloud data platform (Databricks preferred), and version control (Git/GitHub).
- A solid understanding of data modelling, warehousing patterns, and ETL/ELT design.
- To be comfortable working with Postgres or similar relational databases.
- Clear communication - you can explain a data model to a fund manager and discuss trade-offs with an engineer.
- A bias toward shipping: you’d rather get something working and iterate than spend weeks in design.