Senior Data Engineer, Strava
$175-190k
+ Offers Equity
SQL
AWS
Kubernetes
GCP
Python
Scala
Java
Kafka
Go
Airflow
Azure
Redshift
Spark
Snowflake
Flink
BigQuery
dbt
Mid, Senior and Expert level
San Francisco Bay Area
3+ days a week in office (Strava, CA)
Social network for tracking run & cycle routes
Be an early applicant
Social network for tracking run & cycle routes
501-1000 employees
B2CFitnessSocialSaaSCommunity
Be an early applicant
$175-190k
+ Offers Equity
SQL
AWS
Kubernetes
GCP
Python
Scala
Java
Kafka
Go
Airflow
Azure
Redshift
Spark
Snowflake
Flink
BigQuery
dbt
Mid, Senior and Expert level
San Francisco Bay Area
3+ days a week in office (Strava, CA)
501-1000 employees
B2CFitnessSocialSaaSCommunity
Company mission
To create the most committed, close, and engaged athlete community on the internet.
Role
Who you are
- 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
What the job involves
- 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
- 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
- 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
Our take
Strava has created an online network where runners, swimmers and cyclists can record fitness activities, and compare and compete with their community. It offers both a free and premium subscription service, and also partners with local councils and urban planners who purchase its journey data to map out cities and develop safer cycle paths.
With over 125 million athletes and growing, Strava's popularity is clear. Over the years, it has released new features such as an AI-enabled leaderboard, as well as partnering with Oura to make its software available through Oura's wearables. It has also secured impressive funding and a valuation over $2 billion, and is now pursuing an IPO.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Top investors
-6% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Nov 2020
$110m
SERIES F
Oct 2014
$18m
LATE VC
Total funding: $151.4m
Company benefits
- Stock options
- 100% company paid benefits for employees and families
- Flexible paid time off
- $2,000 annual professional development stipend
- Paid time off for volunteering
- 401(k) plan - with company matching
- $1000 annual gear stipend
- $500 annual gym or coaching reimbursement
- On-site fitness rooms with showers, lockers, and towel service
- Weekly team workouts – including remote employees
- Safe and secure bike storage on-site
- Weekly all hands meetings
- Employee resource groups
- Anti-racism nonprofit company matching program
- Free lunch on Wednesdays on-site
- Regular team happy hours
- Team offsites and company retreats
- Cell phone reimbursement
- Generous industry discounts on gear and activities
- Race entries to events we sponsor
- Snacks and stocked kitchens on-site
- Strava gear
Company HQ
Mission District, San Francisco, CA
Leadership
Michael Martin
(CEO & Board Member)Previously GM/VP at YouTube, GM/VP (Greater China Marketplace) at Nike, SVP (Product & Engineering) at NBCUniversal Media and VP (Engineering) at Disney ABC Television Group.
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