Data Engineer - Databricks Pipelines & Mentorship

Magna International; Estado de México; On-site; MXN 520,000 - 900,000; Full time

Job summary

Magna Powertrain is seeking a highly skilled Data Engineer to design and manage our data infrastructure and analytics platforms. You will implement data models, ETL processes, and pipelines using Databricks and PySpark, while mentoring other engineers and ensuring scalable architecture.

Work with cross‑functional teams, leverage Azure Data Factory and CDC for data workflows, and continuously optimize performance and reliability in a fast‑paced environment.

Qualifications

• Degree in computer science or a similar field and an active professional license is required.
• Plus - Databank Admin experience.

Responsibilities

• Design, develop, and implement data models, ETL processes, and data pipelines using Databricks.
• Provide technical guidance, mentoring and support to data engineers.
• Implement architectural standards to ensure a scalable data environment.
• Collaborate with cross‑functional teams to gather requirements and design solutions.
• Hands-on development of Python/Spark‑based scripts and applications for data processing.
• Utilize Databricks Notebooks, Azure Data Factory, and CDC to orchestrate data workflows.
• Optimize data infrastructure for performance, scalability, and reliability.
• Contribute to DevOps activities including deployment of jobs and infrastructure setup.
• Stay updated with trends in data engineering and analytics.

Skills

English - Advanced business Proficient

Python

PySpark

ETL

Data modeling

Data warehousing

Communication

Mentoring

Education

Bachelor's degree in computer science or similar field

Databank Admin experience

Tools

Databricks Notebooks

Azure Data Factory

Change Data Capture (CDC)

DLT Pipelines

Job description

Magna Powertrain is seeking a highly skilled Data Engineer to design and manage our data infrastructure and analytics platforms. You will implement data models, ETL processes, and pipelines using Databricks and PySpark, while mentoring other engineers and ensuring scalable architecture.

Work with cross‑functional teams, leverage Azure Data Factory and CDC for data workflows, and continuously optimize performance and reliability in a fast‑paced environment.