• Driving critical insights based on analyses for the app product team • Create and own the experimentation process on our owned and operated platforms • Provide actionable insights and recommendations for a variety of groups across the organization • Independently write, implement quality controls, and train others to write complex SQL queries • Share technical expertise with co-workers and teammates • Work with internal teams on how they can best use data for decision-making • Prioritize incoming work based on knowledge of departmental and organizational strategy • Anticipate needs and develop research tools and reporting to address current and future business needs • Deliver recommendations with a clear connection to business decisions • Proactively communicate relevant insights to cultivate a learning culture across the organization

• support product analytics needs for the NPR App, site, and video, and work closely with the Insights Sr Manager of product analytics. • develop and manage measurement plans for NPR's owned and operated platforms using Google Tag Manager, and QCing tags after implementation • independently write SQL queries to provide performance analytics across app, video, and website datasets • maintain primary relationships with stakeholders within Product to support daily analytics needs • partner closely with Data Products to consult on App dashboard and tool design based on stakeholder needs • partner with a sustainable system of self-service for app analytics • support diagnosing and resolving data issues • streamline scheduled queries, data summaries, and other infrastructure • write guides and documentation for app, video, and website analytics

• Lead engagement with senior business stakeholders to define data governance priorities, align on business objectives, and translate needs into actionable data initiatives. • Lead the planning, coordination, and execution of data governance initiatives, ensuring alignment with business priorities, timelines, and governance objectives. • Perform advanced analysis of complex enterprise datasets to identify trends, anomalies, root causes, risks, and opportunities that drive governance decisions, operational improvements, and business outcomes. • Develop executive dashboards, visualizations, and data stories that communicate meaningful insights, governance performance, emerging trends, and business recommendations. • Leverage AI-assisted analysis and productivity tools to improve data discovery, documentation, reporting, and insight generation while ensuring outputs are accurate, explainable, and aligned with data governance standards. • Drive the development, documentation, and maintenance of data governance frameworks, including data quality standards, data lineage, metadata management, and data stewardship models. • Partner with data owners and stewards to ensure accountability for data quality, integrity, and compliance across domains. • Support process assessments and root cause analyses to identify, document, and recommend scalable process and system improvements that enhance data quality and governance. • Support regulatory and compliance initiatives by ensuring adherence to data policies, standards, and controls. • Collaborate with technology, info security, risk, compliance, and business teams to operationalize governance tools and processes. • Mentor junior analysts and contribute to building best practices within the data governance function.

• Own the vision and technical strategy for data engineering at CPI, defining how data is ingested, modeled, and delivered across the organization. • Establish strategy, governance, and architecture to ensure reliability, quality, and health of all data ingestion pipelines. • Lead and develop a team of Data Engineers and Data Analysts responsible for CPI’s data platform and engineering roadmap. • Evolve data governance policies, standards, and quality frameworks across the full data engineering lifecycle. • Scale foundational data infrastructure to accelerate enterprise‑wide AI and generative AI initiatives. • Develop new techniques, reports, and models that improve insights, accessibility, and data‑driven decision making. • Design and develop solutions supporting ingestion, provisioning, and visualization of complex enterprise data for analytics, reporting, and data science. • Create and implement design standards and assurance processes for software, systems, and applications to ensure compatibility and operability of data connections, flows, and storage.