Senior Server Engineer, Data Products

About StravaStrava is the app for active people. With over 200 million athletes in more than 185 countries, it’s more than tracking workouts—it’s where people make progress together, from new habits to new personal bests. No matter your sport or how you track it, Strava’s got you covered. Find your crew, crush your goals, and make every effort count. Start your journey with Strava today.

Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward.

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 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...