Senior Data Engineer

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Position Overview

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. Our users rely on MyFitnessPal to power their health and fitness journeys every day. As a member of our MyFitnessPal Data Science team, you’ll have the opportunity to positively impact those users by creating the predictive models and AI that drives the MyFitnessPal ecosystem. In addition to technical expertise and passion, you’ll find that your teammates value collaboration, mentorship, and inclusive environments.

About Our 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, Amazon RDS, MySQL, Snowflake, Databricks, Elasticsearch, Scala, Python, SQL, Amplitude, Appsflyer, Kubernetes, Docker, Okta, Cursor, Claude Code, GitHub CoPilot. We care more about your general engineering skill than your knowledge of a specific language and or framework.

If you are highly motivated, results-oriented, and thrive in a fast-paced and collaborative environment, please apply. We’re looking to add talent that can help further define the culture we’re creating: consumer-centric, curious, and always innovating.

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.
• Drive adoption of DataOps best practices—data modeling, CI/CD, unit testing, and validation frameworks—ensuring quality and consistency across the data platform.
• Mentor data 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, implementing role-based access controls (RBAC) in alignment with legal, security, and data governance requirements.

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.
• Experience with a variety of distributed and non-distributed, structured and unstructured data stores (e.g. RDS, MySQL, MongoDB, DynamoDB, Redis, S3).
• Extensive experience with Airflow or similar orchestration tool.
• Experience writing high throughput services in Python.
• Experience with high volume, event-driven architecture.
• 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).

Preferred Qualifications:

• Experience with distributed systems and near-real-time processing (e.g. Kafka).
• Experience with modular modeling via dbt, including deploying semantic layer best practices.
• Experience with dataset versioning and CI enforcement.