We are looking for a Senior Data Engineer to join the Data Products team at Strava. The Data Products team sits at the core of Strava's AI strategy, turning Strava's unique community and activity data into reliable, reusable, enriched datasets that power experiences across the app. The team operates at the intersection of data engineering, ML platform engineering, and server engineering, building the pipelines and platform layer that let our proprietary embeddings, algorithms, and models reach athletes at scale.

As a Senior Data Engineer, you'll build and operate the pipelines and access layer that turn raw data, algorithms, and models into production-ready data products used across the app. You'll work closely with ML engineers, data scientists, and product teams to ship data products with strong reliability, freshness, and clear contracts, and you'll contribute to the self-serve tools that make these products easier for other teams to build on.

We follow a flexible hybrid model that translates to more than half your time on-site in our San Francisco office — three days per week.

What You’ll Do:

• Build for a Well Loved Consumer Product: Work at the intersection of Geo and fitness to launch and optimize product experiences that will be used by tens of millions of active people worldwide

• Build and Operate Data Products: Develop and maintain the pipelines, APIs, and platform tooling that expose Strava's derived data products, including embeddings, ranking artifacts, clustering outputs, and enriched activity streams, as reliable, well-documented internal products.

• Contribute to Self-Serve Tooling: Build components of the self-serve interfaces and golden paths that let product and CUJ engineering teams use core data products without deep ML or data engineering expertise.

• Own End-to-End Data Product Delivery: Drive projects end-to-end, from pipeline design and artifact schema through production deployment and monitoring, ensuring correctness, freshness, and reliability of the data products you own.

• Collaborate Across ML, Data Engineering, and Product: Work closely with ML engineers on integrating model outputs into durable, versioned artifacts; partner with Data Platform on compute patterns and cost efficiency; inform product teams on how to consume and leverage these capabilities.

• Build from a rich dataset: Explore and use Strava’s extensive unique fitness and geo datasets from millions of users to extract actionable insights, inform product decisions, and optimize existing features

You Will Be Successful Here By:

• Treating Data Products as Products: Bringing engineering rigor, versioning, contracts, SLAs, monitoring, and deprecation paths to data artifacts and ML insights you own, so downstream teams can depend on them.

• Owning Your Work End-to-End: Taking accountability for the reliability and correctness of the systems you build in production and their adoption by downstream teams, while staying aware of adjacent workstreams so dependencies and timing don’t stall the team’s momentum.

• Collaborating Across Disciplines: Working fluidly with ML engineers, data engineers, data scientists, and product managers to align on artifact semantics, evaluation standards, and consumption patterns.

• Contributing to the Standard: Helping establish best practices for data product development and operational health, and mentoring junior and mid-level engineers on the team. You love to stay current on emerging practices in backend and data engineering and apply them pragmatically, favoring what actually moves the team forward over novelty for its own sake.

• Being passionate about the work you are doing and contributing positively to Strava's inclusive and collaborative team culture and values.

What You’ll Bring to the Team:

• Experience building and operating complex, data-intensive backend systems in production at scale, with a track record of breaking large technical problems into well-scoped, executable work.

• Demonstrated experience building access layers, platform tooling, or internal developer products ideally for large scale data or ML systems with a strong instinct for contract design, versioning, and self-serve patterns.

• Experience building and maintaining production data pipelines and batch/stream workflows using technologies like Spark, Kafka, Flink, Iceberg, Snowflake, or similar.

• Proficiency in backend service development on cloud environments (AWS preferred), using Python, Scala, Go, or equivalent. Solid understanding of distributed systems and containerized infrastructure (Kubernetes, Docker).

• Comfort taking technical ownership within a project or team: making design trade-offs, coordinating with collaborators, and mentoring junior engineers and peers.

• Eagerness to engage with ML concepts such embeddings, classification outputs, model evaluation, GenAI integrations. Bonus points if you are already an ML practitioner.

• Strong communication and collaboration skills with the ability to work effectively with cross-functional partners.

For more information on benefits, please click here.

Why Join Us?

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