Senior Data Engineer

WeightWatchers is a global digital health company and the world’s #1 doctor-recommended, clinically studied behavioral weight health program. They are seeking a seasoned Senior Data Engineer to drive scale, performance, and actionability within their data ecosystem, focusing on building core capabilities for dynamic analytics and automated data workflows.

Responsibilities:

• 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
• Design, build, and scale ELT pipelines that are resilient, efficient, and modular
• Partner with Finance, Product, and Analytics to ensure our data models solve the right problems
• Build and maintain analytics schemas (including Star Schemas) that abstract complex logic into user-friendly datasets
• Lead projects from inception to production, taking accountability for data integrity and the trustworthiness of the platform
• Monitor production health using monitoring tools (e.g. Datadog, Monte Carlo) ensuring our data ecosystem remains robust and reliable
• Act as a technical lead, conduct code reviews, define engineering culture, and champion best practices

Requirements:

• 5+ years in data engineering, with at least 2+ years focused on distributed, large-scale cloud data warehouses
• Proven experience with Snowflake performance optimization and cost-governance. Familiarity with Snowflake Cortex/MCP is a plus
• Deep proficiency in Python, with a focus on writing modular, reusable, and testable code (unit/integration tests) for complex data processing
• Expert-level SQL and a sophisticated understanding of data warehousing methodologies to build performant, scalable analytics layers
• Practical experience with modern CI/CD frameworks (e.g., GitHub Actions, Argo CD) to drive engineering velocity and platform stability
• Expertise in architecting and scaling orchestration-as-code workflows (e.g., Prefect or Airflow) to manage complex dependencies and ensure pipeline resilience
• Deep proficiency in deploying monitoring and alerting frameworks (e.g., Datadog) to maximize system uptime while mitigating alert fatigue
• Experience managing business-critical production pipelines with a focus on uptime, data quality, and defining SLAs
• BS/MS in Computer Science, Information Systems, or a related technical field