• Translate marketing analytics and survey data requirements into scalable data models, automated pipelines, and reporting solutions • Design and develop data warehouses, dashboards, and analytical frameworks to support marketing programs and campaign performance measurement • Build and optimize ETL processes integrating structured and unstructured data sources • Solve complex data integration challenges using advanced SQL techniques and data engineering best practices • Collaborate with data engineers, data scientists, and marketers to define data requirements and present insights clearly and effectively • Conceptualize and evaluate large-scale data architectures, balancing performance, scalability, and operational efficiency • Implement and maintain data security and privacy safeguards, ensuring compliance and resolving data quality issues

• Enjoys designing end-to-end data solutions and having real influence over technical decisions. • Lead the evolution of the data platform. • Support strategic business decisions with high quality, governance, and performance. • Work closely with product, engineering, and business teams. • Help transform data into a competitive advantage. • Design and evolve data architecture (batch and real-time). • Define standards, best practices, and guidelines for modeling and integration. • Participate in technology and tooling selection for data solutions. • Ensure data security, governance, quality, and cataloging. • Support data/analytics squads in architecture decisions. • Review technical solutions (code and architecture reviews). • Collaborate with stakeholders to translate business needs into data solutions.

• Own and evolve our data infrastructure, including pipelines into our data warehouse • Manage and improve cloud infrastructure and DevOps workflows • Ensure platform reliability so product and design teams aren’t pulled into backend or operational firefighting • Support internal applications that rely on trusted, accessible data • Partner closely with Product on roadmap planning, scoping, and technical tradeoffs • Identify and resolve platform bottlenecks before they slow the team down • Share progress, risks, and improvements early and clearly

• Develop and maintain data pipelines and transformations across the stack. Starting from ingesting transactional data into the data lakehouse to refining data up the medallion data architecture. • Tune performance, storage layout, and cost-efficiency across our data storage and query engines. • Help design and implement new data ingestion patterns and improve platform observability and reliability. • Partner with engineering, product, and operations teams to deliver well-structured, trustworthy data for diverse use cases. • Help establish and evolve best practices for our data infrastructure, from pipeline design to OpenTofu-managed resource provisioning. • Help design and implement a data governance strategy to secure our data lakehouse.