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
📌 Data Engineer (México)
🏢 Chubb
📍 México