Senior Metrics & Insights Lead

We're looking for a Senior Metrics & Insights Lead to join our global Analytics organization and serve as the technical lead for analytics within our Perioperative Care Coordination solution. This role lives in the space between a product question and a working analytical answer - taking the problem and initial story a PM brings and applying analytical rigor, navigating stakeholder alignment, and deep data & domain understanding to create something delighting, defensible, and trustworthy.

In this role, you will work closely with a wide variety of stakeholders and engineering partners to craft not just insightful dashboards and analytical tools, but also the core metric layer that lets downstream users increasingly answer their own questions. As the technical lead for the analytics pod in PCC, you're who people call to unblock and keep things moving — scoping and refining work sprint to sprint, guiding the dashboards and insights that get built on top of your definitions, and holding ultimate technical responsibility for what the team ships. This role is ideal for someone who enjoys ambiguity at the start of a problem, asks hard questions early, and takes ownership of analytical correctness before work moves downstream.

At Qventus, Analytics is treated as a first-class product, rather than a secondary output. As a Metrics & Insights Lead, you will:

• →Partner with PMs to turn an initial problem and story into an analytically rigorous product, with metrics defined precisely enough for engineers to build against
• →Drive exploratory analysis and POCs in early-stage product discovery to bring analytical rigor and stakeholder alignment early into ROI stories and support data driven product development
• →Build and steward the core metric layer for the solution, grounding definitions in hands-on data exploration, navigating cross-functional alignment when interpretations differ, and creating durable documentation that downstream users and future teams can trust
• →Lead the solution analytics pod as its technical owner to scope and sequence work sprint to sprint, guide dashboard and insight design, and keep the team unblocked and moving
• →Own analytical quality for the solution, setting the bar for rigor, surfacing risks before they become rework, and holding ultimate technical responsibility for what the team ships

• Hands-on expertise with SQL (dbt preferred), and modern data warehouse environments to perform exploratory data analysis, quality assessments, and debug metric definitions
• Sharp data intuition and analytical storytelling with ability to translate ambiguous questions into well-defined metrics and specifications
• Deep expertise with modern BI and analytics tooling - clear understanding of how definitional choices flow through to dashboards, semantic layers, and downstream consumption
• Excellent cross-functional stakeholder management and communication. Ability to drive alignment when interpretations differ and to explain complex analytical tradeoffs to both technical and non-technical audiences
• Demonstrated technical leadership and project management instincts, including scoping and sizing analytics work, sequencing it across sprints, and keeping a team focused and unblocked

• 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 in environments where consistency across teams, solutions, or customer implementations is critical.
• Experience with data logic implemented in dbt and/or data visualization in Sigma
• Experience using knowledge management systems to document metric definitions, analytical decisions, stakeholder alignment, and implementation-ready requirements.
• Experience contributing to analytics QA processes, validation workflows, peer review practices, or release readiness checks for customer-facing analytics.
• Exposure to AI-enabled analytics, natural language analytics, semantic layers, or agentic analytics workflows, especially where high-quality metric definitions and trusted analytical context are required for safe and useful AI outputs.