1 month ago
Base Salary
$175k - $190k/yr
Responsibilities
- Design, build, and operate foundational data systems and shared data assets for analytical, operational, and business use cases.
- Build scalable data ingestion and transformation frameworks across data lakes and data warehouses.
- Develop reusable data engineering tools, frameworks, and abstractions for building, testing, deploying, and operating pipelines.
- Design durable domain data models for core business areas such as users, subscriptions, and activities.
- Build data governance, privacy, regulatory, deletion, retention, access, and lifecycle-management workflows.
- Improve data platform reliability and observability through 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 engineering, analytics engineering, software engineering, data science, security, privacy, and infrastructure teams on data architecture and engineering standards.
Requirements
- 3–5+ years of professional experience in Data Engineering, Data Infrastructure, Software Engineering, or a related field, including ownership of production data systems.
- Strong SQL and data modeling expertise, including dimensional, normalized, or domain-oriented models for large-scale analytical systems.
- Experience building and operating ETL/ELT and data processing systems with dbt, Airflow, Spark, or similar frameworks.
- Experience creating reusable tooling, frameworks, or abstractions for data pipelines and transformations.
- Proficiency in at least one general-purpose programming language such as Python, Scala, Java, or Go.
- Understanding of modern data warehouse and data lake architectures and experience with systems such as Snowflake, Databricks, BigQuery, Redshift, Iceberg, or Delta Lake.
- Experience processing large datasets, including schema evolution, normalization, deduplication, backfills, incremental processing, and data quality.
- Understanding of data governance, lineage, retention, deletion, access control, PII handling, and GDPR or privacy requirements.
- Experience implementing production-grade data quality, monitoring, alerting, testing, and observability.
- Ability to make independent technical decisions about data architecture, modeling, ingestion, transformation, storage, reliability, scalability, and maintainability.
- Comfort working with cloud infrastructure such as AWS, GCP, or Azure.
- Experience with Kafka, Flink, other streaming systems, Kubernetes, open table formats, data catalogs and lineage systems, schema management, CDC, or internal data engineering platforms is a plus.
Benefits
- Flexible hybrid work model requiring more than half of the time onsite, with three days per week in Strava’s San Francisco office.
- Benefits information is provided through a linked Strava benefits document.
- Strava provides reasonable accommodations for individuals with disabilities and is an equal opportunity employer.
Tech Stack
Amazon RedshiftApache AirflowApache FlinkApache KafkaApache SparkAWSAzureDatabricksdbtGoGoogle BigQueryGoogle Cloud PlatformJavaKubernetesPythonScalaSnowflakeSQL
Categories
Data Engineering
About Strava
Strava builds a fitness-tracking and social platform for runners, cyclists, and other athletes on iOS, Android, and the web. The company operates a freemium subscription business with paid training, routing, and safety features, plus APIs and Strava for Business partnerships. Founded in 2009 and headquartered in San Francisco, it is privately held and used by over 200 million athletes across more than 185 countries.