Senior Data Engineer

Strava•San Francisco, CA

8d•$175,000 - $190,000•Hybrid

About The Position

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.

Requirements

3–5+ years of professional experience in Data Engineering, Data Infrastructure, Software Engineering, or a 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.
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.
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.
Experience processing and transforming large datasets, including handling schema evolution, data normalization, deduplication, backfills, incremental processing, and data quality.
Understand 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.
Can independently reason about data architecture and make sound technical decisions around modeling, ingestion, transformation, storage, reliability, scalability, and maintainability.
Comfortable working with cloud infrastructure such as AWS, GCP, or Azure and understand the infrastructure that supports large-scale data processing systems.

Nice To Haves

Experience with Kafka, Flink or other streaming systems
Kubernetes
Iceberg or other open table formats
data catalogs and lineage systems
schema management
CDC
internal developer platforms for data engineering

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.