Senior Analytics Engineer

We're looking for a Senior Analytics Engineer to head up one of our analytics pods and help build analytical products people trust You're the analytical partner our product teams count on They bring the user, the problem, and the direction, and you own what it takes to answer it well: defining the right metrics, weaving a coherent story and visualization design, and ensuring the rigor that makes people trust the result From there, you translate that into the technical substance the pod builds against: context rich metric definitions, dashboard requirements, and the validation that makes it hold up You will lead the analytics pod in the day to day and hold ultimate accountability for the quality of what is delivered within the product request You do the initial project scoping and plan creation, then manage the ongoing tactical sprints, ticketing, and rituals in partnership with your pod’s engineers, and help to keep delivery moving - leaning in to do hands‑on work where needed You’ll report into the Analytics Program Lead and assist in setting the analytics standards for the organization If you like telling stories with data, leading small teams, and are passionate about rigorous, usable analytics, this is that role At Qventus, Analytics is treated as a first‑class product, rather than a secondary output. As an Analytics Tech Lead, you will: Partner with PMs to turn a product idea and initial story into a technically defensible analytics product, defining metrics precisely, shaping the dashboard narrative, and setting the validation methodology, and surfacing the gaps in the design before anything gets built Lead your analytics pod as its technical owner: scoping key initiatives, running sprints, ticketing, leading project management for the pod, and holding the bar for what ships while keeping things unblocked Build and steward the metric definitions and semantic context for your pod’s scope, grounded in hands‑on data exploration and documented so downstream users, future teams, and AI agents can trust them Own analytical quality for your pod, setting the bar for rigor and correctness, catching flawed definitions and misleading framings before they reach a customer, and holding ultimate technical responsibility for what the pod ships Bring analytical rigor into early product discovery, supporting the exploratory analysis and POCs that pressure‑test ROI stories and surface what is really provable before the team commits to building Benefits

Paid parental leave for all new parents Generous HSA contribution Paid disability and life insurance Participation in a 401(k) Wellness + Tech stipends Stock options Comprehensive health benefits with 90% of the cost covered for employees Education and professional development reimbursement Versatile critical thinking, with demonstrated ability to ramp up quickly and translate general skills into tactical enablement, even without deep domain expertise Sharp, experienced analytical thinking, with a track record to build out high-quality, narratively complete analytical components, not just running ad hoc analyses Strong data visualization design & BI development skills (Sigma or similar) Demonstrated ability to partner with PMs as a trusted peer, turning rough ideas into defensible products and pushing on the thinking without friction Hands‑on expertise with SQL and modern data warehouses (DBX preferred): deep enough to run analysis, support and debug metric definitions, and set the technical standard for your pod Experience leading delivery for a small team: comfortable owning sprints, sequencing, and coordination, and getting good work out of a pod without becoming the bottleneck Experience leading dashboard development end to end, from design through validation and including hands‑on build Expertise in the healthcare domain and familiarity with healthcare operational data, clinical workflows, hospital operations, or common healthcare measurement challenges Familiarity with data governance, metric governance, or semantic layer concepts, especially where consistency across teams, solutions, or customer implementations is critical Deeper technical expertise with hands‑on data modeling and transformation experience and standards setting (dbt preferred) Experience contributing to analytics QA processes, validation workflows, peer review practices, or release readiness checks for customer‑facing analytics

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