Senior Data Engineer

Join Strava as a Senior Data Engineer and help build reliable, scalable data systems that support analytics, data science, and critical business use cases. In this role, you will design and operate data pipelines, build high-quality domain data models, improve data transformation workflows, and ensure data accuracy and governance. You will also contribute to data ingestion, quality, privacy, and the evolution of our data warehouse and data lake.

• Design and operate data pipelines, build high-quality domain data models, and improve data transformation workflows.
• Build and evolve scalable data ingestion and transformation frameworks that move, process, clean, standardize, and organize data.
• Develop reusable data engineering tools and abstractions that improve how engineers build and operate data pipelines.

• 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 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 have experience building and operating ETL/ELT and data processing systems using technologies such as dbt, Airflow, Spark, or similar frameworks
• 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 have experience developing reusable tooling, frameworks, or abstractions that improve how data pipelines and transformations are built, tested, deployed, or operated
• 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 experience processing and transforming large datasets, including handling schema evolution, data normalization, deduplication, backfills, incremental processing, and data quality
• You can independently reason about data architecture and make sound technical decisions around modeling, ingestion, transformation, storage, reliability, scalability, and maintainability
• 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
• 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 are comfortable working with cloud infrastructure such as AWS, GCP, or Azure and understand the infrastructure that supports large-scale data processing systems