• Help Product Teams Make Data-Informed Decisions • Analyze product usage and customer journey data to identify meaningful patterns and opportunities to improve product outcomes • Partner with product managers to identify key product questions and ensure analysis and reporting support product planning, prioritization, and decision-making • Translate complex data into clear, actionable insights that inform product decisions • Contribute analysis that helps teams evaluate trade-offs, risks, and opportunities • Measure Product Performance and Impact • Implement measurement plans for new features, experiments, and product initiatives • Analyze product adoption, engagement, and usage patterns • Help evaluate whether product changes achieve intended customer and business outcomes • Surface opportunities to improve product performance based on data trends • Build Dashboards and Reporting • Develop and maintain dashboards that track product health and key performance metrics • Ensure dashboards are clear, intuitive, and aligned with shared metric definition • Create repeatable reporting that enables teams to independently monitor performance • Partner with stakeholders to ensure reporting supports real decision-making needs • Enable Teams to Use Product Data • Coach product teams through interpreting analytics dashboards and reports • Provide guidance on how to use product analytics tools and metrics effectively • Create documentation and usage guidance that enables self-service reporting • Provide day-to-day support on product data, metrics, dashboards, and analytics tool usage • Promote Reliable and Trustworthy Product Data • Model established standards for product data collection, metrics, and reporting • Help identify and resolve data quality issues • Partner with business intelligence, data engineering, and other data teams when product data needs alignment with enterprise reporting • Contribute to clear documentation of data sources, data definitions, and metrics • Work Across the Product Data Ecosystem • Analyze product data using tools such as Pendo, Power BI, and Snowflake • Analyze and incorporate data from product development tools such as Jira to help teams understand delivery progress and product outcomes • Collaborate with product, UX research, and business intelligence partners to connect product insights with broader organizational data

• Own Upstream’s enterprise data, analytics, and AI strategy • Define and execute a 3-year analytics roadmap aligned to revenue, cost, and quality KPIs • Lead, mentor, and grow a team of 4–6 analysts and engineers • Design and deploy enterprise dashboards, predictive models, and automated insights using Microsoft Power BI + Fabric • Integrate data from EHR (RainTree), RCM, etc. • Implement real-time analytics using Azure Data Lake + Synapse • Champion AI/ML adoption (e.g., demand forecasting, therapist scheduling, outcome prediction) • Foster cross-functional partnerships (Clinical, Revenue Cycle, Operations, Finance)

• Live by and champion all of our core values (#ownership, #empathy, #day-one, and #humility). • Build and maintain durable, documented, tested models in dbt that power GTM reporting (e.g., pipeline, funnel, conversion, bookings, forecasting, rep performance, account health). • Design dimensional models (facts/dimensions), metrics definitions, and “one version of the truth” datasets to make analysis repeatable and trustworthy. • Develop and maintain Looker explores, dashboards, and standardized reporting that enable self-serve by the GTM organization. • Implement data quality checks, monitoring, and model documentation so stakeholders can confidently act on results. • Partner with data engineering on upstream improvements (instrumentation, source system hygiene, pipeline reliability). • Collaborate with GTM stakeholders to clarify business questions and translate them into analysis plans, metrics, and decision frameworks. • Perform analysis using SQL (and Python as needed) to identify trends, diagnose performance, and recommend actions (e.g., funnel bottlenecks, win-rate drivers, deal velocity, territory/segment opportunities). • Build GTM narratives that connect data to decisions—clearly communicating insights with business context to leaders and cross-functional partners. • Proactively monitor performance and adoption signals to surface risks, anomalies, and opportunities—then drive stakeholders toward resolution. • Identify high-leverage GTM “insight bottlenecks” and design data products to solve them (e.g., standardized KPI hubs, automated pipeline risk alerts, self-serve funnels, guided explorations, executive views). • Leverage LLMs/AI to accelerate insight workflows—examples might include: • AI-assisted “metric explainers” embedded into dashboards • Natural-language Q&A experiences over curated GTM datasets • Automated insight summaries / anomaly narratives for weekly GTM reviews • Guided self-serve templates (“answer this question in 3 clicks”) for common GTM use cases. • Establish success metrics for data products (e.g., reduced time-to-insight, increased self-serve usage, fewer ad-hoc requests, improved forecast accuracy). • Work independently in ambiguous environments—framing problems, generating hypotheses, and leading toward outcomes. • Manage multiple priorities and communicate clearly through Slack, docs, and live stakeholder readouts. • Contribute to team standards (modeling patterns, documentation, code review, stakeholder intake). • Mentor other analysts through collaboration, query/design feedback, and best practices (where applicable to your team structure).

• Support the maintenance of financial data processing (SQL) and create reports (SQL Server Reporting Services, Power BI) • Work on initiatives to improve current and future processes • Collaborate with business and technology stakeholders to design business intelligence reports, metrics, visualizations, and dashboards • Analyze existing business systems and identify ways to automate by leveraging technology to address inefficiencies • Learn various business processes and cross-functional workflows to define system requirements, write specifications to solve problems, and execute on solutions • Create technical documentation • Continuously look for ways to improve, automate, and scale current processes