Design and implement scalable data pipelines and analytics systems that transform customer feedback and product traces into actionable insights across AI-powered legal products
Job Summary
Design and implement scalable data pipelines and analytics systems that transform customer feedback and product traces into actionable insights across AI-powered legal products.
Build data workflows to enable the deployment of automated evaluation metrics as production analytics to continuously track product quality, detect errors, and alert teams to regressions before they impact customers.
We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
Matching Summary
Design and implement scalable data pipelines and analytics systems that transform customer feedback and product traces into actionable insights across AI-powered legal products.
Salary
Base: $146,800 USD - $272,600 USD (NYC, SF, LA, Irvine); $127,400 USD - $236,600 USD (Other US); $140,000 CAD - $175,000 CAD (Ontario); Bonus/Equity: May be eligible for Annual Bonus; Benefits: Industry Competitive Benefits
Skills & Requirements
Must-have
Build and deploy product analytics infrastructure
Enable AI evaluation at scale
Establish data governance and quality standards
Drive metric development
Support cross-functional teams
Python and SQL programming skills
Modern data stack technologies
Nice-to-have
Experience with AI/ML systems evaluation
Familiarity with LLM applications
Experience building analytics for SaaS applications
Knowledge of data visualization tools
Background in building self-service analytics
Key Requirements
8+ years of professional experience
Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or related technical field
Experience with cloud platforms (e.g., AWS)
Experience with data warehousing, data modeling, and analytics infrastructure
Strong understanding of data governance, data quality, and security best practices