Senior Data Engineer

WeightWatchers is a global digital health company. 

WeightWatchers is a global digital health company and the world’s #1 doctor-recommended, clinically studied behavioral weight health program. For sixty years, we have led the industry by blending science and community to help millions of people build sustainable healthy habits.

As the science of weight health rapidly evolves, so does WeightWatchers. We are redefining the category by developing new clinical pathways for GLP-1 medication access, creating specialized behavioral programs for members on weight-loss medications, and integrating medical care with our proven habit-change framework. By combining these clinical breakthroughs with our digital-first community, we are uniquely positioned to lead the future of weight health care.

We are seeking a seasoned Senior Data Engineer to drive scale, performance, and actionability within our data ecosystem. You will strengthen and scale our Python and Snowflake-based infrastructure, building the core capabilities required to support dynamic analytics and our transition toward more automated, self-service data workflows.

We need an engineer who can adapt to a dynamic product environment, who can ask the ‘why’ behind the ‘what’ to ensure we are building the right solutions.

Key Responsibilities

• Data Enablement: Support the analytics layer by developing core Looker views and building out our agentic AI infrastructure to derive automated self-service capabilities and reduce reporting bottlenecks.
• Pipeline Architecture: Design, build, and scale ELT pipelines that are resilient, efficient, and modular.
• Cross-Functional Collaboration: Partner with Finance, Product, and Analytics to ensure our data models solve the right problems.
• Data Modeling: Build and maintain analytics schemas (including Star Schemas) that abstract complex logic into user-friendly datasets.
• End-to-End Ownership: Lead projects from inception to production, taking accountability for data integrity and the trustworthiness of the platform.
• Operational Excellence: Monitor production health using monitoring tools (e.g. Datadog, Monte Carlo) ensuring our data ecosystem remains robust and reliable.
• Technical Leadership: Act as a technical lead, conduct code reviews, define engineering culture, and champion best practices.

Requirements

• Experience: 5+ years in data engineering, with at least 2+ years focused on distributed, large-scale cloud data warehouses.