• Able to build complex visualizations (dashboards) using multiple charts to tell a story • Able to develop complex SQL queries (e.g., UNION, subqueries, CTEs, etc.) • Proficient in at least one data visualization tool (e.g., Metabase, Looker, Tableau, etc.)

• Assist in maintaining scalable, well-documented dbt models that support regulatory reporting and platform safety monitoring. • Help build and optimize data pipelines in dbt, ensuring they meet the high standards of auditability required for compliance. • Collaborate with legal, policy, and engineering teams to help translate complex transparency requirements into technical data solutions. • Implement data definitions and documentation standards to improve data quality and support long-term maintainability of our safety metrics. • Participate in structured reviews of existing analytics infrastructure to identify performance improvements and alignment with reporting needs. • Support the development of our self-service ecosystem by contributing to intuitive semantic layers that serve a broad stakeholder base. • Help implement validation frameworks that enable performance tracking and early detection of data quality issues.

• Create and articulate a compelling value proposition for AWS migration, clearly demonstrating its transformational benefits to client executives. • Define and execute technical migration and modernization strategies in collaboration with highly technical teams. • Deliver modern architectures, including microservices and event-driven architectures, using managed services and cloud-native capabilities. • Engage with clients’ development, infrastructure, security, and IT operations teams to identify repeatable patterns and architectures for cloud migration. • Identify platform improvement opportunities and influence future iterations of the AWS platform and the Migration Acceleration Program.

• Build and maintain dbt models that transform raw data from multiple sources into clean, tested, well-documented datasets. • Partner with data scientists, engineers, and product teams to translate ambiguous questions into durable data models rather than one-off queries. • Improve data quality across the stack by writing tests, defining expectations for critical models, and triaging issues when something looks wrong. • Document models, metrics, and lineage so engineers, analysts, and partner organizations can self-serve with confidence. • Use Python where it's the right tool — for orchestration, ad-hoc work, or transformations that don't belong in dbt. • Raise the bar on analytics engineering practices, including code review, modeling conventions, CI for data, and documentation standards. • Perform additional engineering and data duties as needed to support the broader team.