This role pays less than
61%
of similar roles. Most pay
$138,325–$214,000
— the shaded band above.
At the midpoint, this role pays about
$156k
versus about
$176k
for comparable roles.
Based on 240 similar postings.
Employer
About General Motors (GM)
General Motors (GM) is a leading American multinational automotive corporation founded in 1908 and headquartered in Detroit, Michigan.
General Motors (GM) currently has
176 open roles
on FindRole.
Listed pay typically runs
$158,000–$245,000
across 61 roles with salary data.
The Senior Data Engineer – Agentic AI, Automation, and Data Platforms serves as a senior individual contributor within the data engineering team. This role focuses on building reliable, scalable, and secure data platforms and pipelines in Azure Databricks to support AI, analytics, and operational use cases. You will transform raw data from multiple source systems into structured products for LLM applications, Vector Search, and RAG workflows. Key responsibilities include designing batch and streaming pipelines, establishing CI/CD patterns, and building automation using Cursor, Claude, and Databricks agents. Required technical skills include Python, Scala, SQL, Apache Spark, and Delta Lake. You will solve complex performance and reliability problems while mentoring team members to improve engineering delivery and productionize intelligent data experiences across the enterprise.
What does a Data Engineer earn?
Median $162000 from 188 postings across 59 companies.
Design, build, and productionize scalable data pipelines and products in Azure Databricks.
Transform raw data from multiple sources into structured products for AI, analytics, and LLM applications.
Build automation and reusable workflows using tools like Cursor, Claude, and Databricks agents.
Design governed data patterns for Vector Search, RAG, and other AI-enabled applications.
Optimize batch and streaming pipelines for feature engineering, training, and model scoring.
Establish engineering patterns for CI/CD, data quality, observability, security, and cost management.
Solve complex data engineering problems regarding performance, reliability, and data quality.
Mentor team members through technical guidance, design reviews, and knowledge sharing.
What we're looking for
Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or related field, or equivalent experience.
5+ years of relevant professional experience.
Strong experience in data engineering, including pipeline development, data modeling, data integration, distributed processing, and production support.
Experience using Python or Scala, SQL, Apache Spark, and modern cloud data platforms; Azure is preferred.
Experience designing and optimizing scalable batch and streaming data pipelines using Databricks, Delta Lake, and lakehouse architecture.
Hands-on experience using AI-assisted development tools such as Cursor, Claude, or GitHub Copilot.
Hands-on experience with LLMs, Vector Search, RAG, Databricks agents, or comparable technologies for production AI solutions.
Master’s degree in Computer Science, Software Engineering, Data Engineering, or related field (preferred).