Staff Data Engineer

At MyFitnessPal, we believe good health starts with what you eat. We provide tools, resources and support to enable users to reach their health goals.

We are looking for a Staff Data Engineer to join the MyFitnessPal Data Engineering team. Our users rely on MyFitnessPal to power their health and fitness journeys every day. As a member of our MyFitnessPal Engineering team, you’ll have the opportunity to positively impact those users with your expertise in the backend systems that drive the MyFitnessPal ecosystem. In addition to technical expertise, you’ll find that your teammates value collaboration, mentorship, and inclusive environments.

About the team:

MyFitnessPal encourages innovation and adoption of the latest technologies available to deliver an amazing experience for our members. Our diverse team of brilliant technologists builds and maintains native mobile applications, a web application, the world’s largest nutrition database, constantly evolving data science and AI/ML assets, the backend infrastructure and data platform required to support these applications and databases, as well as the business systems and data required to manage an awesome company. Technologies and languages we work with include: Airflow, Snowflake, dbt, Git Hub, PlanetScale, Elasticsearch, Scala, Python, SQL, Amplitude, Appsflyer, Kubernetes, Kafka, Docker, Okta and Claude Code. We care more about your general engineering skills and technical leadership than your knowledge of a specific language and or framework.

What you'll be doing:

Design, build, and maintain high-throughput, event-driven data services and orchestration pipelines using Python, Airflow, and Snowflake, enabling reliable analytics and product insights at scale

Architect and evolve core platform components to ensure scalable, secure, and extensible data pipelines to support the organization, including updating ingestion patterns (CDC, Kafka event streaming) to modernize legacy pipelines.

Collaborate with team on development and adoption of DataOps best practices — data modeling, CI/CD, unit testing, and validation frameworks — ensuring quality and consistency across the data platform

Define and evangelize data engineering standards and best practices for the broader organization, reviewing work for other engineers and providing implementation guidance

Further our team's agentic development maturity — increasing the volume of recurring engineering work handled by agents, while prioritizing guardrails and safe AI usage as the foundation of that progress, and bringing the rest of the team along through hands-on collaboration and fostering a culture of learning

Help shape our data governance strategy, including how tools and AI assistants access and query our data safely and consistently

Own cost efficiency and stewardship for Snowflake and pipeline infrastructure, balancing performance and reliability against spend

Mentor data engineers and data analytics engineers, fostering technical growth and driving alignment on architectural patterns, tooling, and resilient system design

Lead cross-functional initiatives that span infrastructure, data services, and observability, to ensure operational excellence

Collaborate with product and engineering teams to proactively identify and solve complex user-facing and system-level challenges across data domains

Develop and maintain secure, compliant, and well-governed data systems, leveraging role-based access controls and data lifecycle management in alignment with legal, security, and data governance needs

Qualifications to be successful in this role:

8 -12+ years in engineering roles, including 3 - 5+ years in data engineering at senior / architect / staff level working end-to-end data platforms

Extensive experience in Snowflake or similar data store

Experience with a variety of distributed and non-distributed, structured and unstructured data stores (e.g. RDS, MySQL, MongoDB, DynamoDB, Redis, S3)

Advanced SQL skills, including experience writing and optimizing complex queries against large-scale, high-cardinality datasets

Extensive experience with Airflow or similar orchestration tool

Experience writing high throughput services in Python

Experience with high-volume, event-driven architecture, including CDC (change-data-capture) and distributed event streaming (e.g. Kafka)