What We Offer :

At Magna, you can expect an engaging and dynamic environment where you can help to develop industry-leading automotive technologies.

We invest in our employees, providing them with the support and resources they need to succeed.

As a member of our global team, you can expect exciting, varied responsibilities as well as a wide range of development prospects.

Because we believe that your career path should be as unique as you are.

Group Summary :

Magna is more than one of the worlds largest suppliers in the automotive space.

We are a mobility technology company built to innovate, with a global, entrepreneurial-minded team.

With 65+ years of expertise, our ecosystem of interconnected products combined with our complete vehicle expertise uniquely positions us to advance mobility in an expanded transportation landscape.

Job Responsibilities :

Job Responsibilities Must Have :

- Non-negotiable skill: 3+ years hands-on: PySpark, Delta Lake, Workflows, Unity Catalog, Databricks.

- Design, develop, and implement efficient and reliable data pipelines using Databricks Delta Live Tables.

- Develop pipelines for structured and unstructured data (i.e documents, JSON, Parquet, Excel) supporting AI and ML consumption downstream.

- Implement and extend data models (i. fact/dimension tables, domain data marts) following designs defined by the Senior DE and AI team.

- Write clean, modular, reusable PySpark and SQL transformation logic that is testable, documented, and deployable via CI/CD.

- Write and optimize complex SQL queries using Databricks SQL for data extraction, transformation, and loading.

- Implement and manage data governance and metadata using Databricks Unity Catalog to ensure data quality and discoverability.

- Collaborate with data scientists, analysts, and other engineers to understand data requirements and deliver appropriate data solutions.

- Monitor, troubleshoot, and optimize existing data pipelines and data infrastructure.

- Stay up-to-date with the latest trends and technologies in data engineering and the Databricks ecosystem.

- Contribute to the semantic layer that powers Power BI dashboards.

Orchestration and Data Ops Should Have :

- Build and manage Databricks Workflows: configuring task dependencies, retry policies, and failure alerting.

- Monitor & manage workspaces as Workspace admins.

- Follow and contribute to CI/CD practices: version control, pull requests, automated testing, and deployment to Dev/QA/Prod environments using Azure DevOps or GitHub Actions.

- Understanding of infrastructure as a service, Terraform and willingness to learn terraform for automation at infrastructure level.

- Package and deploy reusable logic as Python libraries following team standards.

- Monitor pipeline health, investigate failures, and resolve data issues within SLA.

FinOps Awareness Should Have :

- Write cost-conscious PySpark avoiding unnecessary full scans, optimizing joins, using appropriate cluster types.

- Apply Delta table best practices (i.e VACUUM, OPTIMIZE, compaction) to manage storage costs.

- Follow cluster policies defined by platform leads and flag unusual resource consumption.

Experience/Qualifications :

- 4-6+ years of overall data engineering experience.

- 2+ years of hands-on Azure Databricks experience in production environments.

- Demonstrated ability to build and deliver pipelines not just maintain or support them.

- Experience working within a defined architecture and contributing to its improvement.

- Comfortable working with multiple data source types relational, file-based, API.