Architect and build scalable data pipelines and services using Databricks, Spark, and Azure to support advanced analytics and machine learning workflows.
Design and implement industrial-grade data products that ingest, transform, and serve vehicle, battery, and environmental data for product health monitoring.
Collaborate with cross-functional teams to define data requirements and deliver solutions that meet business and technical needs.
Ensure data quality, integrity, and governance across all stages of the data lifecycle.
Optimize performance of data systems and pipelines to support real-time and batch processing.
Develop and maintain documentation, testing frameworks, and CI/CD pipelines for data engineering workflows.
Demonstrate proficiency in Python through hands-on coding, peer reviews, and technical interviews.
Support the integration of new data sources and technologies into the platform, ensuring scalability and maintainability.
Requirements
Master’s degree in Software Engineering, Computer Science, Information Technology, or related field.
5+ years of experience in data engineering, software development, or building data-intensive applications.
Proficiency with Databricks, Spark, SQL, and Python.
Experience designing and deploying data pipelines in cloud environments (preferably Azure).
Strong understanding of distributed systems, data modeling, and ETL best practices.
Ability to translate ambiguous business problems into scalable data solutions.
Excellent communication and collaboration skills.
Demonstrated ability to work in a fast-paced, cross-functional environment.
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