Strava is seeking a Senior Manager, Data Engineering to lead a team focused on building and optimizing data pipelines and data models to enable best-in-class data science, analytics, and self-service business intelligence, and to realize the vision that key decisions and products at Strava are greatly enriched with data to benefit athletes and the business.
Requirements
- 7+ years of experience in data engineering, data platform, or a related quantitative domain.
- 3+ years of experience leading and mentoring high-performing data engineering teams.
- Advanced user of SQL and developed production-grade data pipelines using languages like Python, Scala, or Java.
- Deep experience implementing and maintaining modern ETL/ELT orchestration systems (e.g., Airflow, dbt) and cloud data infrastructure (e.g., Snowflake, BigQuery, AWS, GCP).
- Strong track record of driving data quality, governance, and the implementation of tools for data cataloging and monitoring.
- Understand underlying infrastructure and engineering best practices (e.g., Kubernetes, CI/CD, software development lifecycle) with the ability to influence architectural decisions.
- Experience with data pipeline development, data modeling, and data warehousing
Responsibilities
- Act as the player-coach for our Data Engineering function, fostering a culture of technical excellence and ownership.
- Define the long-term vision and technical roadmap for Strava’s core data platform, including the development of scalable, robust, and efficient ETL/ELT pipelines.
- Drive initiatives to significantly improve the quality, integrity, and availability of Strava’s data assets, implementing best-in-class monitoring and alerting systems.
- Collaborate with Analytics, Data Science, and Product teams to enable advanced data consumption, experimentation, and self-service business intelligence across the company.
- Represent the Data Engineering team in cross-functional strategic planning and collaborate with the broader data community to elevate our technological craftsmanship and data governance standards.
- Translate the company's business objectives into a compelling technical strategy for the Data Engineering team, ensuring all data infrastructure supports business relevance.
- Prioritize and drive execution of projects that significantly improve the scalability, efficiency, and extensibility of our data models and systems.
Other
- 3+ years of experience leading and mentoring high-performing data engineering teams.
- Ability to work in a flexible hybrid model, with more than half your time on-site in our San Francisco office — three days per week.
- Strong leadership and mentoring skills, with the ability to guide team members on complex architectural decisions and best practices for modern data development.
- Ability to proactively anticipate the data needs of a rapidly growing platform, fostering a culture that prioritizes data integrity, security, and privacy.
- Bachelor's degree or higher in a quantitative field (e.g., computer science, mathematics, statistics)
