Senior Data Engineer

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WeightWatchers

Site WebLinkedInTous les Emplois

1001 - 5000 employés

Fondée en 1963

🏥 Santé

🍽️ Alimentation et boissons

Healthcare • Food & Beverage • Health

WeightWatchers est une organisation axée sur la santé qui propose des programmes personnalisés de perte de poids conçus pour créer une relation durable avec la nourriture. Leur approche met l'accent sur l'éducation nutritionnelle, les changements de comportement et un soutien complet grâce à des coachs experts et une communauté de membres. Avec des fonctionnalités telles qu'une application mobile pour suivre les repas et fournir des plans de repas personnalisés, WeightWatchers vise à simplifier une alimentation saine et encourager des changements de mode de vie durables pour améliorer le bien-être.

Description

• 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.

🎯 Exigences

• 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.

🏖️ Avantages

• comprehensive benefits package • annual bonus program