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Jobs / Cookunity

Sr. Analytics Engineer

Cookunity · Latam (Remote)Remotesenior

In short

  • ▸Ingeniero de analytics que construye productos de datos confiables y escalables en Snowflake con dbt.
  • ▸Se enfoca en hacer que los datos sean fáciles de usar, documentados y de alto rendimiento para toda la empresa.
  • ▸Destacado: Tiene autonomía sobre entidades de datos y actúa como propietario de productos de datos con impacto directo en la toma de decisiones.

Fluency in English is required for collaboration with global teams.

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What they ask for

  • ✓Experiencia con dbt y Snowflake
  • ✓Capacidad para diseñar modelos de datos escalables y confiables
  • ✓Habilidades de documentación y comunicación clara
  • ✓Enfoque orientado a usuarios y feedback
  • ✓Experiencia en pruebas y monitoreo de pipelines
  • ✓Mentalidad de propietario de producto y alto agente

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

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Who should you write to at Cookunity?

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About CookUnity: Food has lost its soul to modern convenience. And with it, it has lost the power to nourish, inspire, and connect us. So in 2018, CookUnity was founded as the first-of-its-kind platform that connects the world with the source of truly great food: chefs. Today, CookUnity delivers 50 million meals a year from the industry’s best chefs to homes all over the country. Fresh. Ready-to-eat. And crafted with the passion that nourishes body and soul. Unwilling to stop there, CookUnity is expanding beyond delivery to become an ever-innovating marketplace focused on our singular mission: empower Chefs to nourish the world. If that mission has you hungry in more ways than one, you’ve found the right job posting. About the Team: Our Data Team is a highly dynamic and innovative group that excels in solving complex business challenges and propelling the growth of an enterprise through our analytical expertise. We provide crucial insights that empower individuals across the company to make better decisions, ultimately increasing efficiency. If you have a passion for shaping the future of an organization with your analytical skills, this role offers an exciting opportunity to fulfill your aspirations. The role: Analytics Engineers at CookUnity are data product owners who sit in a single team on a mission to minimize time spent by analysts and data scientists discovering, understanding and preparing data before launching into their work. Each analytics engineer has ownership and autonomy over a set of entities / tables in a domain, collaborating on a single data design from first principles that the team owns together. As their products mature they merchandise them as Tableau Connections, extracts into sheets or any other end point needed to achieve their mission. Some analytics engineers have an interest in data infrastructure and integrations, others in bringing their data to life through interactive visualizations but all have the skills to work with unruly raw data, design analytics friendly data products (incl entities and flat tables) and build them to a production standard that the whole company can rely on. Everyone in data at CookUnity comes with a high agency mindset, a passion for collaborating with others, a pattern of achievement and a sincere interest in investing in their own development. Everyone in data at CookUnity comes with a high agency mindset, a passion for collaborating with others, a pattern of achievement and a sincere interest in investing in their own development. Responsibilities: • Own Core Data Products Design, build, and maintain dbt models in Snowflake that reflect key business logic and serve as reliable sources of truth. Define clear ownership, usage expectations, and roadmaps for your data products. • Build Trustworthy, Scalable Models Develop models that are easy to use, tested, documented, and serve a wide range of stakeholders. Measure success through usage metrics, reduced support needs, and SLA coverage. • Ensure Reliability and Observability Monitor pipelines, track schema changes, and implement testing and alerting to ensure consistent data quality and uptime. • Drive Iteration Through Feedback Collect and act on feedback from users. Use metrics, interviews, and usage logs to continuously improve your data products and documentation. • <strong

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