Analytics Engineering Lead, Rent the Runway

• This role requires a strong individual contributor who is also capable of acting as a technical leader – defining architectural direction, reviewing and guiding contributions from analysts, and partnering closely with Data Engineering on ingestion, orchestration, and performance
• 5+ years owning dbt in production — designing model architecture, testing, documentation, enforcing standards, and reviewing PRs
• 7+ years working with analytical data models and large-scale datasets in modern cloud data warehouses (e.g., Snowflake, BigQuery, Redshift)
• Expert in SQL and in designing data models that support Looker explores and self-service analytics
• Comfortable working with large, high-complexity dbt model — with deep dependency graphs, layered logic, legacy components, and incremental refactoring needs
• Comfortable partnering with Data Engineering on ingestion, orchestration (Prefect), CI/CD, and data quality frameworks
• Experience defining and maintaining Git-based development workflows (branching strategy, PR review processes, testing gates, and controlled release/promotion)
• Able to simplify complex data environments and make scalable architecture decisions
• Strong communicator who can influence across technical and non-technical stakeholders
• Thrives in high-ownership, fast-paced, iterative environments

Desirable

• Data is core to RTR’s strategy and is embedded across product, logistics, customer experience, and business operations
• The Data Analytics team is responsible for delivering accurate, scalable data to the organization, including core dbt models, data definitions, reporting foundations, insights, and self-service analytics
• We are now establishing a dedicated Analytics Engineering function within the Data Analytics team to ensure our data models are scalable, maintainable, well-governed, and aligned to the fast-paced and evolving needs of the business
• We are seeking an Analytics Engineering Lead to take ownership of our core dbt data model, establish modeling best practices, and build the foundation of a scalable Analytics Engineering team
• This is a hands-on leadership role: you will assess the current model, identify areas to simplify and refactor, define a cohesive governance strategy, execute improvements directly, and build a roadmap to evolve the data model over time
• As the function grows, this role will be involved in hiring and mentoring additional Analytics Engineers
• Own the core dbt model: assess current architecture, identify bottlenecks, simplify complexity, and improve maintainability and performance
• Define the technical strategy for the Analytics Engineering function, establishing modeling standards, version control norms, documentation frameworks, and code review practices
• Review PRs from BI Analysts and guide contributions to ensure accuracy, performance, and adherence to modeling conventions
• Serve as the primary bridge between BI (analytics-facing) and Data Engineering (ingestion, orchestration, infrastructure)
• Partner with Data Engineering to improve pipeline reliability, testing coverage, data freshness, and orchestration flows
• Lead and execute large-scale refactors, including preparation for and/or execution of the migration from Snowflake to BigQuery
• Improve and enforce data governance, including data quality checks, model ownership boundaries, and documentation
• Over time, recruit, onboard, and mentor additional Analytics Engineers to scale the function
• Operate with high ownership and autonomy, driving both strategy and execution