Data Engineer - Regional

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

About The Role

We're looking for a Data Engineer to build and maintain the pipelines that power analytics and data products across Chubb. You'll design ETL/ELT workflows on Databricks, turn raw source data into reliable, well‑modeled datasets, and keep those pipelines fast and cost‑efficient as data volumes grow.

Job Description

Data Engineer

About The Role

We're looking for a Data Engineer to build and maintain the pipelines that power analytics and data products across Chubb. You'll design ETL/ELT workflows on Databricks, turn raw source data into reliable, well‑modeled datasets, and keep those pipelines fast and cost‑efficient as data volumes grow.

This role suits someone who is comfortable owning a pipeline end to end — from ingestion through transformation to the tables analysts and data scientists actually query.

What you'll do

• Design, build, and maintain batch and streaming ETL/ELT pipelines using Python, SQL, and PySpark on Databricks.
• Model data across raw, cleansed, and curated layers (medallion architecture) with Delta Lake.
• Ingest data from a range of sources — relational databases, APIs, files, and event streams — including incremental and change data capture patterns.
• Tune Spark jobs and SQL queries for performance and cost: partitioning, file sizing and compaction, caching, join strategies, and shuffle reduction.
• Build data quality checks, validation rules, and monitoring so problems are caught before downstream consumers see them.
• Orchestrate and schedule workflows (Databricks Workflows, Airflow, or similar), with proper retry, alerting, and dependency handling.
• Apply software engineering practices to data work: version control, code review, testing, and CI/CD for pipeline deployments.
• Partner with analysts, data scientists, and business stakeholders to translate requirements into usable data models.
• Document pipelines, data lineage, and design decisions.

Required Qualifications

• [3]+ years of experience in a data engineering or comparable role.
• Strong Python for data processing, automation, and pipeline development.
• Advanced SQL: complex joins, window functions, aggregations, and query optimization.
• Hands- on experience with Databricks and PySpark in a production environment.
• Demonstrated experience designing and operating ETL/ELT pipelines at scale.
• Practical knowledge of performance optimization — able to diagnose a slow or expensive job and explain what you changed and why.
• Solid understanding of data warehousing and modeling concepts (dimensional modeling, slowly changing dimensions, normalization trade-offs).
• Experience with Git and collaborative development workflows.

Nice to have

• Delta Lake internals: OPTIMIZE, Z-ordering, liquid clustering, time travel, VACUUM.
• Databricks features such as Unity Catalog, Delta Live Tables / Lakeflow Declarative Pipelines, Auto Loader, or Databricks SQL.
• Cloud platform experience ([AWS / Azure / GCP]) and its storage and compute services.
• Streaming experience with Structured Streaming, Kafka, or Event Hubs.
• Infrastructure as code (Terraform) and CI/CD pipelines for data workloads.
• dbt or similar transformation frameworks.
• Databricks certification (Data Engineer Associate or Professional).
• Familiarity with data governance, access control, and PII handling.