<div class="content-intro"> <p><a href="https://himalayas.app/companies/parachute-health">Parachute Health</a> 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.</p> <p>Join our team and make a difference in patient care.</p> </div><h3><strong>About the Role</strong></h3><p>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.</p><p>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.</p><h3><strong>Responsibilities </strong></h3><h3><strong>Analytical leadership</strong></h3><ul> <li>Lead end-to-end analyses on high-stakes, often ambiguous questions — from framing the problem through recommendation and impact measurement.</li> <li>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.</li> <li>Set the bar for analytical rigor, methodology, and documentation. By example and by reviewing others' work, not by mandate.</li> <li>Apply statistical methods, experimentation design, and causal inference where they sharpen a conclusion rather than decorate it.</li> <li>Move between business domains and be credible in each, rather than owning one.</li> <li>Partner with leadership to identify where data can drive efficiency, growth, and improve outcomes — including the questions nobody has asked yet.</li> </ul><h3><strong>Partnership with analytics engineering</strong></h3><ul> <li>Work closely with analytics engineers on data modeling. You'll shape what gets built in the warehouse layer, not just consume it.</li> <li>Turn recurring analytical needs into durable models instead of one-off queries.</li> <li>Hold a high standard for canonical definitions — when two reports disagree, you're the one who finds out why.</li> </ul><h3><strong>Team contribution (not people management)</strong></h3><ul> <