Regional Data Engineer: Scale ETL Pipelines

Chubb; Monterrey; Presencial; MXN 360.000 - 720.000; Jornada completa

Descripción de la vacante

Chubb is seeking a Data Engineer to build and maintain pipelines powering analytics and data products. You will design ETL/ELT workflows on Databricks, turning raw data into reliable, well‑modeled datasets while keeping pipelines fast and cost‑efficient as volumes grow.

This role emphasizes end-to-end ownership from ingestion to tables used by analysts and data scientists, with opportunities to optimize performance and implement robust CI/CD for deployments.

Formación

• 3+ years in data engineering or similar role.
• Strong Python for data processing and pipelines.
• Advanced SQL with complex joins, windows, and aggregates.
• Hands-on Databricks and PySpark in production.
• Experience designing and operating ETL/ELT pipelines at scale.
• Ability to diagnose performance issues and optimize.
• Solid data warehousing and dimensional modeling knowledge.
• Experience with Git and collaborative development workflows.

Responsabilidades

• Design, build, and maintain batch and streaming ETL/ELT pipelines.
• Model data across raw, cleansed, and curated layers (medallion).
• Ingest data from relational databases, APIs, files, and streams.
• Tune Spark jobs and SQL for performance and cost.
• Build data quality checks and monitoring to catch issues early.
• Orchestrate workflows with Databricks Workflows or Airflow.
• Apply software engineering practices to data work with CI/CD.
• Partner with analysts and data scientists to translate requirements.
• Document pipelines, data lineage, and design decisions.

Conocimientos

Python

SQL

PySpark

Databricks

Airflow

Git

Data modeling

CI/CD

Herramientas

Databricks

Airflow

Git

CI/CD tooling

Descripción del empleo

Chubb is seeking a Data Engineer to build and maintain pipelines powering analytics and data products. You will design ETL/ELT workflows on Databricks, turning raw data into reliable, well‑modeled datasets while keeping pipelines fast and cost‑efficient as volumes grow.

This role emphasizes end-to-end ownership from ingestion to tables used by analysts and data scientists, with opportunities to optimize performance and implement robust CI/CD for deployments.