• effective analysis is only possible with good data - be an advocate of data accessibility, accuracy and quality • find data nuggets, trends and other analytical goodies that help support our mission, vision, and values and advance our department as a whole • collaboration is key - come prepared to work with a diverse cross-section of technical and business professionals • responsible for delivering data warehouse and analytic solutions by writing ETL packages, developing visualizations, performing data and statistical analyses, and administering systems to deliver information to the health system • serves as a functional and content expert of pipelines to synthesize raw data into actionable information • applies knowledge of relational database skills including advanced SQL knowledge • assists with the design and development of information delivery components including dashboards, reports, and cubes • evaluates data quality and interprets results in a clear, concise manner • develops documentation on requirements, decisions, design, modifications, and any associated maintenance • owns analytics projects and is accountable for collaborating with the business to gather and execute on requirements, which exceed customer expectations • works collaboratively with and supports multi-departments efforts and projects • shares skills, experience, knowledge, and expertise with staff

• Phase 1 - Set up the foundation & Retention Analytics: Define the Data Platform, setting up a cloud data warehouse (Snowflake), DBT (core/cloud), repositories, data alerting, BI system and AI based analytics engineering development working model (claude/cursor/conductor etc) • Ingest core data sources: Atlassian Marketplace API, HubSpot, and product databases (Postgres, MongoDB) • Build the SSOT data layer using dbt: clean, tested, version-controlled transformations • Deliver trusted retention and churn analytics (active customers, logo churn, revenue churn, Net Revenue Retention) • Establish a metric glossary with clear, agreed-upon definitions for core business metrics • Set up pipeline monitoring, data quality checks, and alerting so broken data is caught before it reaches a dashboard • Phase 2 - Build LTV, GTM & Product metrics to help us scale: Build LTV and unit economics models (CAC, ROAS, payback period) by connecting marketing spend data with downstream Marketplace and CRM data • Model the full customer lifecycle: trial → paid → expansion → churn, with cohort and conversion analytics • Fix and automate the Atlassian ↔ HubSpot data sync (reverse ETL), replacing fragile custom scripts with a reliable, maintainable solution • Identify cross-sell opportunities by modeling multi-product adoption across the 70K+ customer base • Build and maintain key dashboards for leadership, marketing, product, and customer success

• Architect and lead end to end tagging and tracking strategy across AXS digital properties; develop, build, test, and deploy tags, triggers, and variables through Adobe Data Collection, ensuring accurate and consistent data collection. • Provide day-to-day management of the data collection integrity, including evaluation, QA, and optimization • Serve as the primary analytics liaison with internal stakeholders (Product, Marketing, Engineering, Design) to orchestrate data collection to align with business needs in a way that ensures data accuracy and completeness. • Build data feeds and pipelines out of core collection systems (Adobe Analytics and Rudderstack) and partner with engineering teams to integrate data to downstream systems. • Own the day-to-day technical data Governance & Quality Assurance Audit analytics setup, validate tracking accuracy, and ensure data integrity across all platforms and environments. • Analyze web and mobile performance metrics using Adobe Analytics, or other digital platforms to identify trends, opportunities, and issues impacting engagement, traffic, and conversion. • Design and Build data feeds and pipelines as well as maintain dashboards and automated reports that provide actionable insights to leadership, Marketing, and Product teams; develop standardized KPIs and reporting cadence.

• Partner with Product, Analytics, and Engineering to understand data needs and translate ambiguous questions into clear, scalable data models • Define, build, and maintain core dbt models that transform raw product data into canonical, well-documented datasets • Own metric definitions and transformation logic to ensure consistency, accuracy, and trust across reporting and analysis • Establish and uphold data quality standards, testing, and expectations around freshness and reliability • Work closely with Product Analysts to enable faster, higher-quality insights and decision-making • Support data consumption in tools like Amplitude and Omni, ensuring data is intuitive and easy to self-serve • Act as a subject-matter expert for analytics engineering, guiding best practices and helping others solve data problems • Contribute to shaping the future direction of our data stack as product complexity and scale increase