Senior Data Engineer at Strava in Strava SF

Job description

• Application
• 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. No matter your sport or how you track it, Strava’s got you covered. Find your crew, crush your goals, and make every effort count. Start your journey with Strava today.
• Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward.
• We are looking for 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 also contribute to areas such as data ingestion, data quality, privacy and GDPR workflows, and the ongoing evolution of our data warehouse and data lake.
• We follow a flexible hybrid model that translates to more than half of your time on-site in our San Francisco office — three days per week.
• Thinking beyond individual pipelines and designing reusable systems, abstractions, and data models that solve common problems across multiple teams and use cases.
• Building well-defined domain data assets with clear semantics, ownership, lineage, and interfaces so downstream consumers can confidently build on top of them.
• Applying strong data modeling principles to represent complex business entities and relationships in ways that are extensible, understandable, and efficient.
• Maintaining a high bar for data correctness, reliability, privacy, and operational excellence across critical production data systems.
• Making thoughtful engineering tradeoffs across data freshness, scalability, storage, compute cost, complexity, and developer productivity.
• Proactively identifying recurring pain points in the data development lifecycle and creating tooling or platform capabilities that eliminate manual work and improve engineering velocity.
• Designing data systems with governance and regulatory requirements in mind, rather than treating privacy and compliance as downstream concerns.
• Bringing software engineering discipline to data infrastructure through testing, modular design, version control, CI/CD, observability, documentation, and code review.

Responsibilities

• Design, build, and operate foundational data systems and shared data assets that serve a broad range of analytical, operational, and business use cases across Strava.
• Build and evolve scalable data ingestion and transformation frameworks that move, process, clean, standardize, and organize data across our data lake and data warehouse.
• Develop reusable data engineering tools and abstractions that improve how engineers build and operate data pipelines, including frameworks and capabilities around technologies such as dbt.
• Design high-quality, durable domain data models — such as user, subscription, activity, or other core business domains — that provide consistent definitions and reusable foundations for teams across the company.
• Build systems and workflows that support data governance, privacy, and regulatory requirements, including GDPR-related deletion, retention, access, and data lifecycle management.
• Improve the reliability and observability of our 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 across both warehouse and data lake environments.
• Partner with data engineers, analytics engineers, software engineers, data scientists, security, privacy, and infrastructure teams to establish scalable data architecture and engineering standards.

Requirements

• You have 3–5+ years of professional experience in Data Engineering, Data Infrastructure, Software Engineering, or a related field, with experience owning production data systems.
• You have strong expertise in SQL and data modeling, including experience designing dimensional, normalized, or domain-oriented data models for large-scale analytical systems.
• You have experience building and operating ETL/ELT and data processing systems using technologies such as dbt, Airflow, Spark, or similar frameworks.
• You have experience developing reusable tooling, frameworks, or abstractions that improve how data pipelines and transformations are built, tested, deployed, or operated.
• You are proficient in at least one general-purpose programming language such as Python, Scala, Java, or Go and are comfortable applying software engineering principles to data systems.
• You understand modern data warehouse and data lake architectures and have worked with technologies such as Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta Lake, or similar systems.
• You have experience processing and transforming large datasets, including handling schema evolution, data normalization, deduplication, backfills, incremental processing, and data quality.
• You understand data governance and data lifecycle concepts such as lineage, retention, deletion, access control, PII handling, and GDPR/privacy requirements.
• You have experience implementing production-grade data quality, monitoring, alerting, testing, and observability for data pipelines and datasets.
• You can independently reason about data architecture and make sound technical decisions around modeling, ingestion, transformation, storage, reliability, scalability, and maintainability.
• You are comfortable working with cloud infrastructure such as AWS, GCP, or Azure and understand the infrastructure that supports large-scale data processing systems.
• 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 is a plus.
• For information on benefits, please click here.

Skills

Required

• SQL
• Data modeling
• ETL/ELT
• dbt
• Airflow
• Spark
• Python, Scala, Java, or Go
• AWS, GCP, or Azure
• Data governance
• Data quality
• Monitoring and alerting
• Observability

Preferred

• Kafka
• Flink
• Kubernetes
• Iceberg
• Data catalogs
• Lineage systems
• Schema management
• CDC