Stellantis is looking to solve the problem of designing, developing, and optimizing scalable data pipelines and platforms to align data engineering initiatives with business goals, fostering innovation, and driving operational excellence across the data lifecycle.
Requirements
- Proven experience in managing data engineering teams and delivering complex data solutions.
- Deep expertise in Big Data technologies (e.g., Spark, Hadoop) and cloud data platforms.
- Advanced proficiency in programming (e.g., Python, Shell) and orchestration tools (e.g., Airflow, Docker).
- Strong command of SQL and NoSQL databases (e.g., Oracle, HBase).
- Experience with CI/CD pipelines, version control (e.g., Git, GitHub), and DevOps practices.
- Experience with Power BI for data visualization and reporting.
- Strong understanding of Kimball star schema data modeling techniques.
Responsibilities
- Lead and mentor a team of data engineers, providing technical guidance and career development.
- Architect and oversee the implementation of robust, scalable data pipelines.
- Ensure data integrity, quality, and compliance across all data engineering processes.
- Collaborate with cross-functional teams to define data strategy, requirements, and delivery timelines.
- Drive the adoption of best practices in data modeling, ETL/ELT design, and data platform optimization.
- Implement and monitor end-to-end data workflows using modern orchestration and automation tools.
- Evaluate and integrate emerging technologies to enhance data engineering capabilities.
Other
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 5+ years of experience in data engineering, with at least 2 years in a leadership role.
- Demonstrated success in building and managing high-performing teams.
- Excellent communication and stakeholder management skills.
- Strategic thinking with a focus on scalability, performance, and maintainability.
