Staff Data Engineer

MyFitnessPal

12h•$170,000 - $230,000•Hybrid

About The Position

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.

Requirements

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)
Experience with a variety of API design patterns ( REST, SOAP, etc)
Strong data ops skills including modeling, CI/CD pipelines, source control, unit testing frameworks, and data validation frameworks
Experience implementing observability via monitoring and alerting
Strong familiarity with infrastructure-as-code (e.g. Terraform)
Experience designing or driving adoption of agentic or AI-assisted development workflows (e.g. structured specs consumed by agents, automated test generation, agent-driven PR pipelines, skill development, AI code review)
Demonstrated experience defining engineering standards or best practices adopted across a team or organization, and cost-conscious ownership of infrastructure/systems

Nice To Haves

Experience with modular modeling via dbt, including deploying semantic layer best practices
Experience with dataset versioning and CI enforcement
Experience with scaling and resilience in data pipelines
Experience in high velocity experimentation organizations
Experience with data cataloging/governance tools (e.g. Secoda, Snowflake Horizon) and metadata stewardship programs
Experience building or evaluating AI-assisted internal tools with deterministic, code-level business-rule enforcement (vs. purely prompt-based vendor AI tools)
Experience with multi-agent orchestration or coordination patterns

Responsibilities

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