• Design, build, and maintain dbt models that transform raw event- and entity-level data into curated, analytics-ready datasets. • Develop, document, and test semantic and reporting layers in Looker (LookML). • Partner with Analysts and cross-functional stakeholders to ensure consistent metric definitions and accurate data pipelines. • Contribute to data modeling best practices, including naming conventions, incremental strategies, and schema evolution. • Implement data quality tests and validation checks to ensure reliability of production datasets. • Monitor and troubleshoot data issues, coordinating fixes with Data Infrastructure and Engineering.

• Design and execute scalable data models and transformation patterns across core data marts that power analytics and AI use cases across the business • Partner cross-functionally to translate business problems into data solutions that support AI-led analytics and intelligent decision making • Define and drive data governance and certification standards that establish trusted, enterprise ready data sets • Define and implement SDLC best practices for data transformation, including documentation, testing and version control to ensure reliability over time • Socialise best practice guides that enable spoke analytics teams to move faster with consistent patterns • Assess existing models and pipelines to identify opportunities to simplify, standardise and improve • Identify and propose new ideas that advance an AI-led analytics experience and improve how insights are generated

• Develop, test, and maintain high-performance dbt data pipelines within our ELT framework. • Own the lifecycle of data products—from understanding the initial business question to deploying the final dashboard or automated insight. • Partner closely with business units (Product, Finance, Ops) to identify value-creation opportunities and translate vague requirements into technical specs. • Build and manage reports that provide 'single source of truth' visibility into company performance at all levels, from executive KPIs to granular operational metrics. • Ensure data integrity and documentation across the warehouse, advocating for best practices in version control and testing.

• Own the data infrastructure and pipelines for business critical product and user data • Conceptualize and build best-in-class internal tools enabling others to increasingly self-serve data needs • Take an AI-first approach to scaling Runway’s data tooling and use • Work with sales and finance to ensure billing, revenue recognition, margin analysis, and other key financial metrics are accurate • Work with product and engineering to ensure critical usage data is available and interpreted in useful ways • Collaborate with trust and safety teams on signals for detecting misuse or abuse • Collaborate with marketing and studios teams to help measure impact of all marketing initiatives • Build and maintain our data pipelines and third party tools that others use for dashboarding and analysis