Data Engineer

Role Overview This role will focus on designing, developing, and supporting Databricks-based pipelines, medallion-layer data products, and enterprise integrations that enable analytics, reporting, and AI use cases. The role also includes building and supporting data movement patterns both into Databricks and between enterprise applications, including solutions that leverage DataStage and related integration technologies. What You Will Do Design, build, and maintain scalable data pipelines in Databricks to ingest, transform, validate, and publish trusted data products for analytics, reporting, and AI use cases. Develop and support end-to-end ETL/ELT workflows using Databricks notebooks, Python, Spark, and SQL, including orchestration, parameterization, error handling, restartability, and performance optimization. Why It Might Be a Fit Strong collaboration skills and a track record of working effectively in cross‑functional teams (data engineers, architects, product owners, business partners, and report and analytics teams). Strong problem‑solving, communication, and ownership skills, with the ability to operate effectively in a fast‑moving environment. Requirements Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Data Science, Engineering, or a related field; or equivalent experience. 5+ years of experience as a data engineer, ETL developer, or integration engineer building production‑grade data pipelines and data products. Hands‑on experience with Databricks for data engineering and analytics enablement, including: Strong SQL skills in Databricks, Experience building and supporting ETL/ELT pipelines in Databricks, Experience developing pipelines using Python, notebooks, DataStage, and scalable data transformation patterns, Experience with workflow orchestration, dependency management, scheduling, monitoring, and operational support of production pipelines. Proven experience designing and implementing dimensional, layered, or medallion‑style data models for analytics and operational use cases. Strong knowledge of data warehousing and ETL/ELT concepts, including how upstream design impacts downstream performance, usability, and trust in data products. Experience integrating data from enterprise applications, especially operational platforms such as ServiceNow. Familiarity with DataStage and application‑to‑application integration patterns. Experience using GitHub for source control, branching, pull requests, collaboration, and release management of data engineering assets. Demonstrated ability to implement data quality, metadata, documentation, and governance practices in production data environments. Benefits medical dental vision Health Savings Account Flexible Spending Accounts retirement savings plan sickness and accident benefits life insurance paid vacation & holidays tuition assistance program employee assistance program GM vehicle discounts #J-18808-Ljbffr