Analytics Automation Engineer (Python/Databricks)

GM•Warren, MI

6d•Hybrid

About The Position

About GM There’s never been a more exciting time to work for General Motors. To achieve our vision of a world with Zero Crashes, Zero Emissions and Zero Congestion, we need people who are passionate about creating safer, better and more sustainable ways for people to get around. About Product Safety Data Analytics (PSDA) Product Safety Data Analytics (PSDA) is part of GM’s safety ecosystem and supports safety decision-making by identifying potential emerging vehicle safety issues through analysis, mining, and monitoring of internal and external data. PSDA works with high-volume structured data and large volumes of unstructured text to detect trends and patterns, support safety cases, and deliver decision-ready outputs to downstream stakeholders. This includes identifying trends and patterns across customer complaints, engineering, crash, telematics, survey, and research data. The Role We are hiring a hands-on builder to develop Python and Databricks tools that eliminate manual work and improve the reliability, speed, and consistency of analytics workflows. You will partner closely with analysts and safety stakeholders in PSDA to translate operational pain points into scalable automation, reusable tooling, and repeatable outputs. This role is ideal for someone who owns problems end-to-end: clarifying requirements, shipping tools, improving performance, and documenting solutions so they can be supported by the team. The work includes applied AI enablement for large-scale unstructured text workflows (AI Query and related tools), and we are looking for candidates who have demonstrated experience using AI in production or operational settings. We prioritize strong builders who can ship and support tools; candidates are not expected to be experts in every preferred area.

Requirements

Strong Python coding ability (debugging, writing maintainable code, building tools).
Experience using Databricks (or similar notebook-based platform) and strong SQL skills.
Demonstrated ability to build tools and workflows end-to-end and improve underperforming solutions.
Practical experience fixing messy data workflow issues (deduplication, text cleaning, special characters/encoding, unstructured text handling, file handling).
Experience applying AI to real workflows (for example unstructured text classification, extraction, summarization, or decision support), including basic quality and reliability practices.
Ability to take ambiguous requests and deliver working, documented solutions with minimal supervision.
Strong communication skills with non-technical partners (clear expectations, status, tradeoffs, and handoffs).
Strong organizational skills and ability to prioritize multiple tasks without sacrificing quality.

Nice To Haves

Hands-on Excel automation using VBA/macros.
Experience with AI and LLM workflows for text classification (prompt patterns, evaluation, consistency improvements, guardrails)
Experience implementing AI agents and workflow automation using enterprise AI tools and/or internal agent frameworks.
Experience improving AI workflow quality and stability (prompt patterns, evaluation/spot-checking, consistency improvements, guardrails, human-in-the-loop patterns).
Experience implementing workflow automation using APIs and/or agent frameworks and enterprise AI tools.
Familiarity with Spark concepts, job scheduling, Git/version control, and basic testing practices.
Background in analytics engineering and data enablement practices (standards, reusable datasets, data quality checks).
Optional BI experience (Power BI/Tableau).
Optional: experience creating and maintaining data pipeline architecture and assembling large, complex datasets that meet business requirements

Responsibilities

Map end-to-end workflows with stakeholders, identify and prioritize automation opportunities (AI, APIs, logic-based automation), quantify impact, and maintain a backlog of improvements.
Build and maintain Python tools in Databricks notebooks that replace manual processes end-to-end, including reusable templates and clear documentation/runbooks.
Enable and troubleshoot AI-assisted unstructured text classification workflows (AI Query, AI agents, and related tools), improve reliability and consistency, and implement agent-based automation where appropriate.
Drive data quality and reliability across data flows, including deduplication, special character and encoding cleanup, file splitting, exports/uploads, performance tuning, and repeatable validation checks
Build and maintain Excel automation for workflows that remain Excel-based (macros and VBA preferred).
Create standardized charts and outputs produced by your tools and notebooks so stakeholders can interpret trends and act quickly.
Optional: publish dashboards and self-serve reporting (Power BI or Tableau) when helpful.

Benefits

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