Senior Data Engineer
Weight Watchers
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Weight Watchers is the global leader in science-backed weight management, offering an integrated support system built for the GLP-1 era that combines scientific expertise, medication, cutting-edge technology, and human connection. With more than 60 years of experience, Weight Watchers is the most studied commercial weight management program in the world, delivered through its No. 1 U.S. doctor-recommended weight-loss program. Its holistic, personalized approach also includes U.S.-based clinical interventions and access to GLP-1 medications when clinically appropriate, and a global network of coaches and community support. Since 1963, the company has led with science to deliver its members the personalized support they need to reach and sustain their goals. Members can access these solutions directly, or through Weight Watchers for Business’ full-spectrum platform for employers, health plans, and payers. In a landscape crowded with contradictory advice, isolating apps, and one-size-fits-all solutions, Weight Watchers offers a proven path forward that is rooted in research, grounded in empathy and designed to help every member feel better in their body and live a longer, healthier life. 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. Snowflake Expertise: Proven experience with Snowflake performance optimization and cost-governance. Familiarity with Snowflake Cortex/MCP is a plus. Technical Mastery: Advanced Software Engineering (Python): Deep proficiency in Python, with a focus on writing modular, reusable, and testable code (unit/integration tests) for complex data processing. Data Modeling & SQL Mastery: Expert-level SQL and a sophisticated understanding of data warehousing methodologies to build performant, scalable analytics layers. Infrastructure & Automation: Practical experience with modern CI/CD frameworks (e.g., GitHub Actions, Argo CD) to drive engineering velocity and platform stability. Workflow Orchestration: Expertise in architecting and scaling orchestration-as-code workflows (e.g., Prefect or Airflow) to manage complex dependencies and ensure pipeline resilience. Observability & Reliability: Deep proficiency in deploying monitoring and alerting frameworks (e.g., Datadog) to maximize system uptime while mitigating alert fatigue. Operational Maturity: Experience managing business-critical production pipelines with a focus on uptime, data…
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