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As a part of data engineering in Digital Services and Experience, you will design and maintain scalable data pipelines and curated data products that enable GM to deliver future-focused, consumer-centric solutions. You will support multiple lines of business (Loyalty, OnStar, Digital Services & Experience, Manufacturing, Growth Businesses) by ensuring reliable, high-quality data is available for decision making and advanced analytics.
Job Responsibility:
Design, build, and maintain data pipelines and ETL/ELT processes using Python, SQL, and Spark (e.g., in Databricks)
Develop curated, production-grade datasets from large, complex data sources to support business and advanced analytics use cases
Implement data quality checks, monitoring, and documentation for data products and pipelines
Work with cross-functional teams to translate business needs into scalable data solutions and resolve data-related technical issues
Identify opportunities for automation and process optimization in data ingestion, transformation, and delivery
Manage and maintain data models, metadata, and data standards, ensuring consistency across systems
Requirements:
Bachelor’s degree in Computer Science, Engineering, Information Technology, or related field
Fluency in English language
Strong experience with big data and processing frameworks (e.g., Spark, Hadoop, Kafka) and cloud platforms (Azure or AWS)
Advanced SQL skills and experience with relational and NoSQL data stores (e.g., Postgres, Cassandra)
Proficiency in Python and experience with Databricks, Spark, and Git/GitHub for version control
Practical experience working with large-scale event or customer data and turning it into reliable, reusable datasets
Demonstrated ability to identify tasks for automation and implement automated solutions
Strong problem-solving, communication, and collaborative skills, with the ability to manage multiple priorities in a dynamic environment