Tech Lead Data Engineer
Aviso de fuente externaen Cloudbeds
Shape data architecture, governance, modeling, and warehouse design for Cloudbeds’ Data Analytics platform using Snowflake, dbt, Matillion, and Sigma.
Salario
No especificado
Ubicación
Mexico City, México
Tipo de empleo
Tiempo completo
Modalidad
No especificado
Tech Lead Data Engineer
Mexico City, México
Descripción del empleo
About the role
Cloudbeds is transforming hospitality with a unified platform that integrates with hundreds of partners and powers hotels across 150 countries. The Data Analytics & BI team runs analytics for the company using Snowflake, dbt, Matillion, and Sigma.
As Tech Lead Data Engineer & Data Architect, you will shape the technical direction, architectural standards, and data governance of the core Data Analytics platform. This is a hands-on leadership role with latitude to make foundational decisions about architecture, platform, governance, and MLOps.
Responsibilities
- Own logical and physical data modeling, warehouse design, and layered curation from staging through intermediate to mart.
- Serve as Data Engineering Tech Lead and help continuously modernize the tech stack.
- Elevate software engineering discipline across data pipelines, including DataOps and CI/CD.
- Optimize compute performance and costs, drive MLOps workflows, and reinforce data quality and security standards.
Datos clave
- The Data Analytics & BI team uses Snowflake, dbt, Matillion, and Sigma.
- The role includes data modeling, warehouse design, and layered curation.
- Responsibilities include DataOps/CI-CD, compute optimization, MLOps, data quality, and security.
Preguntas frecuentes
What does the role involve?
It shapes the technical direction, architecture, and data governance of Cloudbeds’ core Data Analytics platform. It also owns data modeling, warehouse design, and layered curation.
Which tools does the team use?
The team runs analytics using Snowflake, dbt, Matillion, and Sigma.
What are the main technical responsibilities?
The role includes modernizing the tech stack, improving DataOps/CI-CD, optimizing compute performance and costs, driving MLOps workflows, and reinforcing data quality and security standards.