Lead Data Analyst

We're looking for a senior individual contributor who can turn ambiguous business and product questions into the right KPIs, the right data, and ultimately a decision, not just a dashboard. This is a business understanding-first role: given a problem (e. g., why did automation % move, is a new workflow actually working, or what should we expect next quarter), you should be able to figure out what data already answers it, what data doesn't exist yet and how to get it, and what the number actually means for the business. You'll work directly with product, engineering, and leadership; use AI as a core part of your analytics workflow; and own your problem domain end-to-end as a senior IC. This role has no direct reports; senior analysts mentor by practice.

Responsibilities:

• Translate ambiguous business and product questions into well-defined KPIs and metrics; own the definition, not just the reporting.
• Identify existing data sources and design plans to source or build what's missing.
• Run rigorous quantitative analysis (Advanced SQL/Python, statistical methods, data mining) across large and messy datasets to extract decision-relevant insights.
• Design and run experiments (A/B tests, cohort and funnel analysis) to validate hypotheses and build forward-looking business projections.
• Perform root cause analysis on metric movements and anomalies; connect numbers to mechanisms, not just trend lines.
• Use AI tools and agents as a core part of your analytics workflow, with judgment on where AI output can be trusted vs. verified.
• Partner directly with product, engineering, and leadership to turn analysis into decisions; represent data credibly in cross-functional forums.
• Contribute to scaling and maturing the analytics function, including mentoring other analysts.

Requirements:

• 7+ years in analytics, including 1-2 years at a lead/senior owner level.
• Strong hands-on experience in advanced SQL and Python.
• Statistical experience and data mining applied to real business problems.
• Proven track record of defining and owning business KPIs/metrics.
• Direct experience working with product, engineering, and leadership stakeholders.
• Demonstrated depth in using AI in analytics workflows, specific tools, and specific judgment.
• Evidence of driving measurable business impact through data.
• Strong problem-solving and communication skills.
• Background from a strong product- or data-mature company.
• Ability to translate ambiguous business problems into the right KPIs and data.

Preferred:

• Experimentation experience (A/B testing, cohorts, funnels).
• Experience with messy, high-volume datasets.
• Experience helping build or scale an analytics function from an early stage.
• Exposure to healthcare or revenue cycle domains.
• Familiarity with modern data stack (Snowflake, BigQuery, dbt, Airflow).