Strava

Overview

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.

About Strava

Strava 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.

Requirements & Eligibility

• 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).

Key Responsibilities

• 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.
• 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.
• Collaborate Across ML, Data Engineering, and Product: Work closely with ML engineers on integrating model outputs into durable, versioned artifacts.