Review and validate financial data to ensure accuracy and integrity for core AI-powered workflows.
Diagnose complex parsing failures and uncover root causes to improve model performance.
Help design feedback loops between Data Ops and Engineering that drive faster product iteration.
Partner with Product and Engineering to refine our internal Retool-based review systems.
Test new features, prototype automation, and document improvements that reduce review time.
Act as a first-line QA for new product launches and parsers (e.g., broker, insurance, or lending data).
Identify recurring manual steps and propose or build small automations to eliminate them.
Support in designing metrics dashboards that measure SLAs, accuracy, and operational throughput.
Support customer success by ensuring reviewed data meets accuracy and turnaround SLAs.
Collaborate with GTM and Product teams to surface insights from operational data that improve user experience.

Analytical & Curious: You’re good at breaking down ambiguous problems and spotting patterns in data.
Tech-Savvy: Comfortable with digital tools, automation, or low-code platforms (Retool experience a plus).
Fast Learner: You pick up new systems quickly and enjoy figuring things out without needing detailed instructions.
Detail-Oriented but Strategic: You care about accuracy but also about how to fix systems that break.
Bias to Action: You don’t just point out problems — you propose and test solutions.
Educational background: Finance, Economics, Engineering, Data Analytics, or similar (but we value mindset over degree).

You’ll work on real problems, not hypothetical cases — helping make AI systems more reliable.
You’ll collaborate with a small, fast-moving team that values autonomy and learning.
You’ll see your ideas and improvements shipped into production, impacting real customers.
You’ll get exposure to multiple disciplines — fintech, AI, data engineering, and product design.

Data Operations Analyst

Key skills