Senior Data Engineer – Agentic AI, Automation, and Data Platforms

General Motors•Austin, TX

22h•$138,700 - $173,750•Hybrid

About The Position

This role is for a senior individual contributor in Data Engineering who can independently lead complex technical work, apply strong professional judgment, improve processes and delivery patterns, and move quickly from ideas to production solutions. At this level, the individual is expected to operate with minimal guidance, resolve non-standard problems using advanced analytical thinking, take ownership of outcomes, and serve as a technical resource for less experienced team members. The role is anchored in data engineering with a strong focus on automation and Agentic AI. The engineer will build reliable data platforms and use technologies such as Cursor, large language models, Vector Search, Databricks agents, RAG, and similar tools to accelerate engineering delivery and enable intelligent data experiences. The engineer will partner with data scientists and ML engineers as needed to support experimentation and productionize AI solutions, while data engineering and platform delivery remain the primary focus.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or related field, or equivalent experience.
5+ years of relevant professional experience, or equivalent knowledge and experience.
Strong experience in data engineering, including pipeline development, data modeling, data integration, distributed processing, and production support for enterprise data platforms.
Experience using Python or Scala, SQL, Apache Spark, and modern cloud data platforms; Azure is preferred, and AWS or GCP experience is also considered.
Experience designing, building, and optimizing scalable batch and streaming data pipelines using Databricks, Delta Lake, and medallion or comparable lakehouse architecture.
Hands-on experience using AI-assisted development or automation tools such as Cursor, Claude, GitHub Copilot, or comparable platforms to improve engineering productivity and delivery.
Hands-on experience with LLMs, Vector Search, RAG, Databricks agents, or comparable technologies used to build or enable production AI solutions.
Demonstrated ability to work independently, move quickly through ambiguity, influence technical decisions, and deliver measurable improvements in quality, reliability, efficiency, or business value.

Nice To Haves

Experience building or operating Databricks agents, Vector Search solutions, LLM applications, RAG workflows, Genie spaces, Glean integrations, or similar Agentic AI platforms.
Experience applying evaluation, monitoring, access controls, guardrails, and governance to AI or agent-enabled solutions.
Experience partnering with data scientists or ML engineers on feature engineering, experimentation, model development, model serving, or productionization of AI solutions.
Experience with infrastructure as code, APIs, data contracts, platform automation, or reusable engineering libraries and templates.
Experience in manufacturing, supply chain, automotive, planning, or another complex operational domain.
Demonstrated mentoring, technical leadership, and process improvement impact consistent with a Level 7 senior individual contributor role.
Master’s degree in Computer Science, Software Engineering, Data Engineering, or related field.

Responsibilities

Design, build, and productionize reliable, scalable, and secure data pipelines and data products in Azure Databricks that support AI, analytics, and operational use cases.
Transform raw data from multiple source systems into trusted, well-structured data products for analytics, model development, LLM applications, Vector Search, and AI agents.
Build automation and reusable engineering workflows using tools such as Cursor, Claude, LLMs, Databricks agents, and related technologies to improve development speed, testing, documentation, troubleshooting, and operational efficiency.
Design and enable governed data, retrieval, and semantic patterns for Vector Search, RAG, Databricks agents, Genie, Glean, and other AI-enabled applications.
Build and optimize batch and streaming pipelines, including feature-ready, training, inference, and model-scoring data workflows, in partnership with data science teams when needed.
Establish practical engineering patterns for CI/CD, automated testing, data quality, lineage, observability, security, cost management, and production support.
Solve complex data engineering, performance, reliability, and data-quality problems with strong ownership, urgency, and sound technical judgment.
Contribute to technical direction, reusable standards, and delivery practices across teams; influence adoption through working examples and measurable outcomes.
Mentor team members through technical guidance, design reviews, knowledge sharing, and strong engineering practices.