Senior Server Engineer (Data Products)

Join Strava, a well-loved consumer product at the intersection of geo and fitness. 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. You'll work closely with ML engineers, data scientists, and product teams to ensure strong reliability, freshness, and clear contracts. This is a hybrid role with more than half your time spent on-site in our San Francisco office. Enjoy a range of benefits including 100% company-paid benefits for employees and families, flexible paid time off, and a $2,000 annual professional development stipend.

• Construire et exploiter les pipelines et la couche d'accès qui transforment les données brutes, les algorithmes et les modèles en produits de données prêts pour la production.
• Collaborer avec les ingénieurs ML, les scientifiques des données et les équipes produit pour expédier des produits de données avec une forte fiabilité, fraîcheur et contrats clairs.
• Contribuer aux outils en libre-service qui facilitent l'utilisation de ces produits par d'autres équipes.

• Comfort taking technical ownership within a project or team: making design trade-offs, coordinating with collaborators, and mentoring junior engineers and peers
• Strong communication and collaboration skills with the ability to work effectively with cross-functional partners
• 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
• 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)
• 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
• Eagerness to engage with ML concepts such embeddings, classification outputs, model evaluation, GenAI integrations. Bonus points if you are already an ML practitioner