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Strava is a global community platform for active people with over 200 million athletes across 185+ countries. The Data Products team sits at the core of Strava's AI strategy, turning the company's unique community and activity data into reliable, reusable, enriched datasets that power experiences across the app.
In this Senior Data Engineer role, you will build and operate the pipelines and access layers that transform raw data, algorithms, and models into production-ready data products used across the app. You'll work at the intersection of data engineering, ML platform engineering, and server engineering, collaborating closely with ML engineers, data scientists, and product teams to ship data products with strong reliability, freshness, and clear contracts.
Key responsibilities include:
- Building and maintaining production data pipelines, APIs, and platform tooling that expose derived data products including embeddings, ranking artifacts, clustering outputs, and enriched activity streams
- Developing self-serve interfaces and golden paths that enable product and engineering teams to use core data products without deep ML or data engineering expertise
- Owning end-to-end data product delivery from pipeline design and artifact schema through production deployment and monitoring
- Collaborating across ML, data engineering, and product teams to integrate model outputs into durable, versioned artifacts
- Exploring Strava's extensive fitness and geo datasets to extract actionable insights and optimize existing features
- Establishing best practices for data product development and mentoring junior and mid-level engineers
You will treat data products as products, bringing engineering rigor, versioning, contracts, SLAs, monitoring, and deprecation paths to the artifacts and ML insights you own. You'll take accountability for reliability and correctness in production while staying aware of adjacent workstreams to prevent dependency bottlenecks.
The role operates on a flexible hybrid model with three days per week on-site in the San Francisco office.
REQUIREMENTS:
- 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 for large-scale data or ML systems, with strong instinct for contract design, versioning, and self-serve patterns
- Experience building and maintaining production data pipelines and batch/stream workflows using technologies such as Spark, Kafka, Flink, Iceberg, Snowflake, or similar
- Proficiency in backend service development on cloud environments (AWS preferred), using Python, Scala, Go, or equivalent languages
- 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 as embeddings, classification outputs, model evaluation, and GenAI integrations (bonus if already an ML practitioner)
- Strong communication and collaboration skills with ability to work effectively with cross-functional partners
