• Participar ativamente das definições e implementações da plataforma de dados • Manutenção de plataforma já existente • Construção de soluções técnicas de consumo, exploração, integração e entrega de dados • Depuração problemas de produção relacionados a pipelines de dados e bancos de dados • Otimização de processos existentes para melhorar a precisão e o desempenho de pipeline e aplicações orientados a dados.

• Create a shared vision and cohesive roadmap for the future of this product area • Drive alignment, set goals, build understanding and motivate others • Guide new product ideas from initial concept all the way through launch • Understand our customers and their journey through the product by engaging with them directly and often • Use data to identify opportunities to improve our customers’ experience • Own both the developer-facing schema and ingestion experience and the end-user-facing event management surfaces • Define how behavioral event data gets structured, queried, and surfaced across HubSpot's product • Shape the data collection primitives that non-technical customers rely on to understand what their users are actually doing

• Design, develop, and maintain ETL workflows to ingest, transform, and deliver data across multiple systems. Ensure pipelines are scalable, reliable, and efficient. • Design schemas, dbt models, and database structures optimized for dashboards and analysis. • Maintain a thorough, meticulously documented, and tested codebase that can be scaled across large datasets. • Collaborate with team members and stakeholders to understand data needs and deliver solutions.

• Develop and maintain data pipelines that process healthcare claims, enrollment, and related administrative data • Own components of data pipelines end-to-end, from raw data processing through final analytic outputs • Translate technical specifications and business rules into well-structured, maintainable code • Work with large-scale healthcare datasets (tens to hundreds of millions of rows) within the VRDC's secure computing environment • Perform data validation, quality checks, and reconciliation to ensure analytic accuracy • Identify opportunities to improve code quality, reduce duplication, and strengthen testing, validation, and documentation • Ensure reproducibility and traceability of results across data refreshes and measurement years • Communicate technical findings, methodology, and data issues clearly to actuarial and consulting staff • Manage concurrent workstreams across multiple measurement years and data refresh cycles