Senior Data Products Engineer - Data Pipelines & ML

TOGETHXR; San Francisco, Northern (CA, KY); On-site; USD 180,000 - 240,000; Full time

Job summary

Strava is seeking a Senior Data Engineer to join the Data Products team in San Francisco. You will build and operate pipelines and the access layer turning raw data and models into production-ready data products used across the app.

You’ll collaborate with ML engineers, data scientists, and product teams to ensure reliability, freshness, and well-documented internal products, with a flexible hybrid model and on-site focus in SF.

Qualifications

• Experience building and operating complex, data-intensive backend systems at scale.
• Experience building access layers, platform tooling, or internal developer products.
• Production data pipelines with Spark, Kafka, Flink, Iceberg, Snowflake.
• Backend development in AWS and Python/Scala/Go; containers (Kubernetes, Docker).
• Ability to collaborate across ML, data engineering, and product.
• Interest in ML concepts and embeddings; bonus if already an ML practitioner.
• Strong communication and cross-functional collaboration.

Responsibilities

• Build and operate data products and the access layer used across Strava.
• Contribute to self-serve tooling and golden paths for internal teams.
• Own end-to-end data product delivery from design to deployment and monitoring.
• Collaborate with ML, data engineering, and product teams to align artifact semantics.
• Build from Strava’s large fitness and geo datasets to derive insights.

Skills

Data engineering

Python

Go/Scala

Kubernetes

Distributed systems

ML concepts

AWS

Communication

Tools

Spark

Kafka

Flink

Iceberg

Snowflake

Docker

Job description

Strava is seeking a Senior Data Engineer to join the Data Products team in San Francisco. You will build and operate pipelines and the access layer turning raw data and models into production-ready data products used across the app.

You’ll collaborate with ML engineers, data scientists, and product teams to ensure reliability, freshness, and well-documented internal products, with a flexible hybrid model and on-site focus in SF.