Analytics Engineering Lead Brooklyn, NY ( HQ

Rent the Runway (RTR) is transforming the way we get dressed by pioneering the world’s first Closet in the Cloud. Founded in 2009, RTR has disrupted the $2.4 trillion fashion industry by inspiring women with a more joyful, sustainable and financially‑savvy way to feel their best every day. As the ultimate destination for circular fashion, the brand offers infinite points of access to its shared closet via a fully customizable subscription to fashion, one‑time rental or ownership.

RTR offers designer apparel and accessories from hundreds of brand partners and has built in‑house proprietary technology and a one‑of‑a‑kind reverse logistics operation.

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 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.

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 Big Query.
• 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.

• 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, Big Query, 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…