Python / Data Engineer
Build data and tooling that makes research reproducible and production dependable.
Mission
This role sits at the core of research velocity and correctness. You’ll build reliable pipelines, data contracts, and internal tooling that makes it easy to test ideas, reproduce results, and operate systems safely.
Outcomes (First 90 Days)
01
Improve data reliability: cleaner pipeline, stronger validation, clearer lineage/docs.
02
Build or enhance a tool that improves reproducibility or operational visibility.
03
Establish strong data hygiene patterns: contracts, checks, clear ownership.
Interview Loop
1
Intro call
Alignment on role and expectations
2
Technical rounds
Role-specific
3
Practical working session
Practical problem solving
4
Founder conversation
Standards, ownership, culture
Responsibilities
- Build and maintain data pipelines for research and production inputs.
- Implement data validation, quality checks, and failure handling.
- Create tooling: internal APIs, dataset/versioning patterns, workflow utilities.
- Optimize for reliability first; performance where it matters.
- Partner with researchers to define data needs without brittle assumptions.
- Maintain documentation so systems are operable and predictable.
Required Skills (Must-Have)
- Strong Python; clean, testable, production-grade code.
- Experience with pipelines, reliability patterns, and debugging.
- Comfort with contracts/schemas/validation and monitoring.
Bonus (Nice-To-Have)
- Distributed compute, orchestration, streaming, or high-volume pipelines.
- Observability tools and dashboarding.
- Systematic trading / time-series heavy domains.
How We Work
Flat teams, clear ownership, evidence-based reviews.
Clear expectations, direct feedback, strong support.
Problems are solved systematically, not politically.
How To Apply
Apply using the form or email us. We read every application. If there's a fit, you'll hear back from us.
