Senior Data Analyst, Ambience Healthcare

• This is a high-impact, hands-on role for someone who loves turning complexity into clarity
• If you’re excited to bring structure to ambiguity, build scalable tools from scratch, and drive data literacy across teams—we’d love to meet you
• 5+ years in a full-time analytics/BI role at a high-growth company or data-focused organization
• Expert-level SQL for complex joins, window functions, query optimization
• Strong proficiency in Python or R for data wrangling, analysis, and light ML/forecasting
• Mastery of at least one modern BI platform (e.g. Power BI, Looker, Tableau, Sigma)
• Experience partnering cross-functionally with teams like Product, CS, Ops, GTM, and Finance
• Comfort working with Data Engineering—reviewing dbt PRs, aligning on schema design, and ensuring SLA compliance
• Exceptional communication and data storytelling skills—comfortable briefing execs and mentoring power users
• Thrive in fast-paced, ambiguous environments and take initiative without waiting for direction
• Think like a systems builder—crafting scalable tools, not just one-off analyses
• Communicate clearly with both technical and non-technical partners
• Care about making a real-world impact, especially in mission-critical domains like healthcare
• Are energized by teaching others how to use data, not just analyzing it yourself

Desirable

• We’re hiring a Senior Data Analyst to help lay the foundation of a modern analytics function at Ambience
• You’ll partner closely with our Data Engineering team to transform raw data into trusted insights, intuitive dashboards, and self-service tools that support every function across the company—from Product and Clinical teams to Customer Success, Finance, and GTM
• You’ll be one of our first analytics hires, setting the bar for how we define metrics, ship dashboards, and embed analytics into daily decisions
• Build intuitive, actionable dashboards for internal teams (Product, Customer Success, Execs) and external stakeholders (customers, partners)
• Define and maintain trusted KPI definitions for core business metrics—MAU, feature adoption, churn risk, ROI impact, and more
• Launch predictive models (e.g. churn forecasting, ROI projections) and surface insights in tools teams already use (e.g. Slack, Notion)
• Develop account health, telemetry, and ROI dashboards used to guide QBRs, product roadmap, and revenue strategy
• Collaborate with data engineers on semantic layers, testing frameworks, and scalable data modeling
• Lead data bootcamps, publish a searchable data catalog, and champion self-service analytics across functions
• Translate stakeholder questions into clear KPIs, hypotheses, and decision-ready analysis