• Design, build and improve Power BI dashboards that answer recurring and ad hoc strategic questions • Develop and extend the data models that power these dashboards and analyses, utilizing dbt within a Microsoft Fabric environment • Build and maintain pipelines that populate these data models • Validate data quality and accuracy across claims, eligibility, clinical, HR, and engagement data, investigating and resolving discrepancies • Support custom and ad hoc analyses on strategic benefits questions

• Diseñar, desarrollar, probar y mantener procesos ETL y tuberías de datos • Extraer, transformar y cargar datos de múltiples sistemas de origen en almacenes de datos y lagos de datos • Desarrollar y optimizar consultas SQL, procedimientos almacenados y objetos de base de datos • Monitorear trabajos ETL y solucionar problemas de integración de datos • Asegurar la precisión, integridad y calidad de los datos a lo largo del ciclo de vida de los datos • Colaborar con ingenieros de datos, analistas, arquitectos y partes interesadas del negocio para comprender los requisitos de datos • Realizar validación de datos, conciliación y ajuste de rendimiento • Crear y mantener documentación técnica para procesos ETL y flujos de datos • Soportar despliegues en producción y actividades de mantenimiento continuo.

• You build and maintain ETL/ELT pipelines from various source systems into PostgreSQL and BigQuery. • You ensure data quality, e.g., through automated sanity checks, reconciliation processes, and systematic debugging across our data pipelines. • You support the further development of our web tracking and analytics infrastructure, ensuring that data is reliable and ready for use. • You work on Python and/or R scripts, including for Google Ad Manager, operational automations, and internal data processes. • You assist in operating our job portal and help keep data feeds, interfaces, and workflows stable and efficient. • You create analyses and dashboards that are used directly by teams such as Sales, Finance, and Product to enable data-driven decisions.

• Build the Data Foundation • Design and operate the end-to-end gameplay analytics ecosystem. • Define tracking, storage, processing, and reporting architecture. • Own data correctness and make judgement calls when quality checks flag anomalies. • Drive AI-Native Analytics • Build the semantic layer that translates raw gameplay data into validated gameplay and business concepts. • Develop AI-driven automated reporting, dashboards, and insight-generation workflows. • Evaluate and implement AI tools, agents, and workflows that improve analytics speed, reliability, and depth. • Review AI-generated code and analytics on critical paths before they reach production. • Generate Product Intelligence • Identify opportunities, risks, anomalies, and trends across gameplay and player behavior. • Support product, balancing, design, and leadership teams with data-driven recommendations. • Translate complex datasets into clear, actionable insights. • Set the Agentic Bar • Help define how the wider engineering organization adopts agentic practices. • Establish standards for tooling, review processes, quality gates, and what 'done' means when agents generate most of the implementation.