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Strava is the world's largest community for active people, with over 200 million athletes across 185+ countries. The company is backed by Sequoia Capital, TCV, Madrone Partners, and Jackson Square Ventures.
You will join the Data Team to design and operate reliable, scalable data systems that support analytics, data science, and critical business use cases across the organization. This is a senior individual contributor role focused on building foundational data infrastructure and shared assets.
Key Responsibilities:
- Design, build, and operate foundational data systems and shared data assets serving analytical, operational, and business use cases across the company
- Build and evolve scalable data ingestion and transformation frameworks using technologies like dbt, Airflow, and Spark
- Develop reusable data engineering tools and abstractions that improve how engineers build and operate data pipelines
- Design high-quality, durable domain data models (user, subscription, activity, etc.) that provide consistent definitions and reusable foundations
- Build systems and workflows supporting data governance, privacy, and regulatory requirements including GDPR compliance
- Improve reliability and observability of the data platform through automated testing, data quality checks, lineage, monitoring, and alerting
- 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 standards
Success Profile:
- Think beyond individual pipelines to design reusable systems, abstractions, and data models solving common problems across multiple teams
- Build well-defined domain data assets with clear semantics, ownership, lineage, and interfaces
- Apply strong data modeling principles to represent complex business entities and relationships
- Maintain high standards for data correctness, reliability, privacy, and operational excellence
- Make thoughtful engineering tradeoffs across freshness, scalability, storage, compute cost, complexity, and developer productivity
- Proactively identify recurring pain points and create tooling to eliminate manual work and improve velocity
- Design data systems with governance and regulatory requirements in mind from the start
- Bring software engineering discipline to data infrastructure through testing, modular design, version control, CI/CD, observability, and documentation
Workplace: 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: 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
