About the role
Parachute Health is transforming post-acute care as the leading digital ordering platform for medical equipment and supplies. We connect major health systems, health plans, and suppliers to help patients get the life-saving products they need at home. Since launching, we've connected 300,000+ clinicians and 3,000+ supplier locations across all 50 states and helped 15M+ patients. What started as a DME ePrescribing tool has become the order management platform of choice for home medical equipment. Join our team and make a difference in patient care.
About
the Role The Lead Data Analyst is the senior individual contributor on our Data Analytics team — a Level IV role on our analytics ladder, with no direct reports. You lead through expertise: setting the analytical bar, mentoring analysts, and being the person leadership comes to when a question needs a real answer rather than a guess. Data Analytics sits under Revenue Operations, but we're a central function that supports the entire organization — clinical operations, supplier performance, payor and network data, product, finance, and go-to-market. The work is broad by design. The core of the job is being an excellent analyst: taking an ambiguous question from any corner of the business, building a rigorous analysis, and delivering a conclusion people can act on. We're hiring for analytical strength and range, not depth in one domain.
Responsibilities Analytical leadership Lead end-to-end analyses on high-stakes, often ambiguous questions — from framing the problem through recommendation and impact measurement. Design dashboards and reporting frameworks that let stakeholders answer their own routine questions without opening a ticket — and recognize when a dashboard is the wrong answer. Set the bar for analytical rigor, methodology, and documentation. By example and by reviewing others' work, not by mandate. Apply statistical methods, experimentation design, and causal inference where they sharpen a conclusion rather than decorate it. Move between business domains and be credible in each, rather than owning one. Partner with leadership to identify where data can drive efficiency, growth, and improve outcomes — including the questions nobody has asked yet. Partnership with analytics engineering Work closely with analytics engineers on data modeling. You'll shape what gets built in the warehouse layer, not just consume it. Turn recurring analytical needs into durable models instead of one-off queries. Hold a high standard for canonical definitions — when two reports disagree, you're the one who finds out why. Team contribution (not people management) Mentor analysts on technique and business judgment. You don't manage anyone — you're the person newer analysts learn from. Translate findings into clear, concise language for both technical and non-technical audiences. If a new hire couldn't repeat the takeaway back to you, it isn't ready to ship. Influence senior stakeholders, including being the one who says "the data doesn't support that" to someone who wanted a different answer.