About
When a product runs on agents, bad data doesn't stay in a dashboard. It shows up as a wrong answer to a customer. The data team owns everything that prevents that: pipelines, quality checks, core data models and the infrastructure behind product decisions.
This is a late-stage, well-funded AI company growing above 100% year on year. The data team 5+ engineers in New York, most with a year or more together, so context runs deep. You'd join as the data surface grows with each new product.
System stability is a real engineering problem here, not a side task. The pace is intense and the office is five days a week. The team wants strong software engineers who happen to be deep on data, not pure pipeline specialists.
What you'll do
- Own the data pipeline end to end: design, build and run the ETL and the infrastructure behind it
- Build large-scale pipelines that process high volumes reliably, with clear quality standards
- Design the core data objects and models used across the company
- Set up data quality monitoring and alerting that catches problems before customers do
- Optimise data flow for product, operations and analytics teams
- Build the reporting and metrics datasets that clients see
- Debug data problems at the source, across services, rather than patching downstream
What you'll need
- 4+ years of professional data engineering experience
- Strong coding foundations in Python, Java and/or SQL
- ETL design and implementation at scale, plus data modelling and database design
- Working knowledge of data warehousing, ideally Snowflake
- Experience with data quality management and monitoring tools
- High ownership and comfort with ambiguity in a small, fast team
- On-site in New York, five days a week
Optional Bonus
- Experience with real-time data streaming platforms
- Familiarity with data visualisation tools
Shortlisted candidates will be contacted within 48 hours.
