• Ensure data quality, consistency, and integrity across collection-related datasets and reports • Monitor and investigate data discrepancies, identify root causes, and implement fixes • Handle and resolve data-related requests and issues from internal stakeholders (collections team, operations, etc.) • Maintain and improve existing data pipelines, queries, and dashboards • Perform regular data validation and reconciliation checks • Collaborate with engineering and data teams to address systemic data issues • Document data definitions, processes, and known issues • Proactively identify opportunities to improve data quality and reporting efficiency

Role Description This individual will work cross-functionally across Product, Marketing, Base Management, and Finance to build dashboards and deliver insights that reduce churn and increase ARPU across our consumer organization. They will define and standardize key metrics, translate business questions into analysis, and communicate clear, actionable recommendations to product owners and senior stakeholders. The role will also help identify growth and retention opportunities through experimentation, customer segmentation, and funnel/cohort analysis, ensuring teams can consistently measure impact and make data-informed decisions. - Build and maintain executive-ready dashboards (Tableau) to monitor acquisition, engagement, retention, churn, and ARPU performance. - Partner with Product, Marketing, Base Management, and Finance to define KPIs/metric definitions and create a single source of truth for reporting. - Churn and retention analytics (cohort, funnel, and lifecycle analysis) to identify drivers, quantify impact, and prioritize initiatives. - Develop customer segments and behavioral profiles to tailor retention and monetization strategies and improve campaign/product targeting. - Design measurement plans for experiments and feature launches; evaluate lift/ROI and recommend next actions based on statistically sound analysis. - Build and maintain models (e.g., propensity, forecasting, scenario analysis) to understand ARPU drivers and predict retention and revenue outcomes. - Synthesize analysis into clear narratives and recommendations for leadership, including ongoing readouts on churn, ARPU, and initiative impact. - Improve data quality and usability by partnering with Data Engineering on instrumentation, data validation, and scalable dataset design. Qualifications - 4+ years of experience in data analytics (consumer subscription/retention analytics a plus) - Advanced SQL skills and experience working with large, complex datasets - Strong Tableau skills (building scalable dashboards and self-serve reporting) Requirements - Standing Occasionally: 0-33% - Walking Occasionally: 0-33% - Sitting and Stationary: Continuously: 67-100% - Bending: Occasionally: 0-33% - Crouching: Occasionally: 0-33% - Carrying: Occasionally: 0-33% - Reaching: Occasionally: 0-33% - Lifting - Lowering >1-15 lbs: Occasionally: 0-33% - Repetitive Hand Action: Medium Dexterity: Continuously: 67-100% - Fine Manipulating: Frequently: 34-66% Benefits - Medical, Dental, Vision Insurance Plans - 401K Plan - Health & Flexible Savings Account - Life and AD&D, Spousal Life, Child Life Insurance Plans - Educational Assistance Plan

• People Data Analysis: Utilizing data from internal and external resources and applying statistical methods to find patterns in data related to recruitment, attrition, engagement, organizational structure, pay practices and more. • Connect Data with Business Need and People Strategy: Partnering with People Business Partners, Centers of Excellence, and Business leadership, identify data trends and incorporate business context to make insightful connections between experience and data. • Strategic Advisor: Offer practical and informed recommendations to improve policy, workforce planning, talent development, retention, etc. to achieve business goals. • Reporting and Visualization: Collect and synthesize data into user-friendly dashboards to support data-driven decision-making.

• Develop, maintain and optimize data extraction, transformation and loading (ETL) processes; • Analyze, model and process large volumes of data; • Build queries, stored procedures and routines for integration and information provisioning; • Support Business Intelligence, Analytics and Data Science initiatives; • Ensure data quality, integrity and governance; • Identify and resolve issues related to performance and availability of data environments; • Produce studies, KPIs and analyses to support strategic decision-making; • Collaborate with business areas, data engineering and systems development teams.