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Strava is the world's largest community of active people, with over 200 million athletes in more than 185 countries. The company is backed by Sequoia Capital, TCV, Madrone Partners, and Jackson Square Ventures.
We are seeking a Senior Data Engineer to join our Data Team and help build reliable, scalable data systems that support analytics, data science, and critical business use cases across Strava.
In this role, you will design and operate data pipelines, build high-quality domain data models, improve our dbt and data transformation workflows, and help ensure data is accurate, well-governed, and easy to use. You will contribute to data ingestion, data quality, privacy and GDPR workflows, and the ongoing evolution of our data warehouse and data lake.
Key responsibilities include:
- Design, build, and operate foundational data systems and shared data assets serving analytical, operational, and business use cases
- Build and evolve scalable data ingestion and transformation frameworks across data lake and data warehouse
- Develop reusable data engineering tools and abstractions, including dbt frameworks and capabilities
- Design high-quality, durable domain data models (user, subscription, activity, etc.) providing consistent definitions for teams across the company
- Build systems and workflows supporting data governance, privacy, and regulatory requirements including GDPR-related deletion, retention, access, and data lifecycle management
- Improve reliability and observability of the data platform through automated testing, data quality checks, lineage, monitoring, alerting, and operational tooling
- Optimize large-scale data processing and storage for performance, maintainability, scalability, and cost
- Partner with data engineers, analytics engineers, software engineers, data scientists, security, privacy, and infrastructure teams to establish scalable data architecture and standards
You will succeed by thinking beyond individual pipelines to design reusable systems and abstractions, building well-defined domain data assets with clear semantics and ownership, applying strong data modeling principles, maintaining high standards for data correctness and reliability, making thoughtful engineering tradeoffs, proactively identifying pain points and creating solutions, designing systems with governance in mind, and bringing software engineering discipline to data infrastructure.
The role follows a flexible hybrid model with more than half your time on-site in the San Francisco office—three days per week.
REQUIREMENTS:
- 3–5+ years of professional experience in Data Engineering, Data Infrastructure, Software Engineering, or related field, with experience owning production data systems
- Strong expertise in SQL and data modeling, including experience designing dimensional, normalized, or domain-oriented data models for large-scale analytical systems
- Experience building and operating ETL/ELT and data processing systems using technologies such as dbt, Airflow, Spark, or similar frameworks
- Experience developing reusable tooling, frameworks, or abstractions that improve how data pipelines and transformations are built, tested, deployed, or operated
- Proficiency in at least one general-purpose programming language such as Python, Scala, Java, or Go, with comfort applying software engineering principles to data systems
- Understanding of modern data warehouse and data lake architectures and experience with technologies such as Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta Lake, or similar systems
- Experience processing and transforming large datasets, including handling schema evolution, data normalization, deduplication, backfills, incremental processing, and data quality
- Understanding of data governance and data lifecycle concepts such as lineage, retention, deletion, access control, PII handling, and GDPR/privacy requirements
- Experience implementing production-grade data quality, monitoring, alerting, testing, and observability for data pipelines and datasets
- Ability to independently reason about data architecture and make sound technical decisions around modeling, ingestion, transformation, storage, reliability, scalability, and maintainability
- Comfort working with cloud infrastructure such as AWS, GCP, or Azure and understanding of infrastructure supporting large-scale data processing systems
Plus (nice-to-have): Experience with Kafka, Flink or other streaming systems; Kubernetes; Iceberg or other open table formats; data catalogs and lineage systems; schema management; CDC; or internal developer platforms for data engineering.
