Systems Engineer - Data and Insights

GM•Warren, MI

5d•Hybrid

About The Position

The Finance Data and Insights team in the Business Applications (Finance, Legal & SAP Engineering – FLSE) organization is responsible for empowering Finance and Legal teams by delivering reliable and secure AI-driven tools that streamline workflows, accelerate decision making, surface actionable insights, and unlock measurable productivity gains across the product lifecycle. As a Systems Engineer, you will be responsible for developing data intensive and AI-enabled data solutions and tools that support the Finance team. You will work with complex data requirements and deliver solutions using industry standard practices across SQL, Databricks and Python. You will design and build software and data solutions based on detailed requirements and system specifications, with a strong focus on data quality, performance, and reliability. You will help define patterns and best practices for SQL, Databricks (DBX), and AI/ML integrations within Business Applications and play a key role in ongoing modernization and cloud initiatives. You will also develop agents and solutions using Glean and Genie, driving transformation in ways of working through agents and automated workflows.

Requirements

Bachelor’s Degree in Computer Science, Software Engineering, Information Systems, Engineering, or a related field, OR equivalent experience.
2–5 years of systems engineering experience delivering data-intensive and analytics-focused solutions, with proven technical and professional skills in SQL, Python/PySpark , and cloud data platforms such as Databricks .
Experience working with complex SQL queries and user-defined functions in Databricks , including performance tuning and optimization against large datasets
Hands-on experience with AI agents and/or skills, such as: Building or configuring agents in platforms like Glean, Genie , or IDE-based agents ; and/or Authoring Markdown-based skill/rule files (e.g., SKILL.md, rules.md ) that guide AI behavior.
Familiarity with Model Context Protocol (MCP) or similar patterns for connecting agents to tools, APIs, and data sources, with a strong interest in deepening expertise in this area.
Comfort working with Markdown for technical documentation, skill definitions, and agent specifications.
Strong problem solving skills with the ability to break down complex technical and data challenges into clear, actionable steps and deliver high-quality solutions.
Excellent written and verbal communication skills with the ability to collaborate with both technical and non-technical stakeholders.
Demonstrated ownership mindset, accountability for quality, and focus on delivering measurable value to internal customers.

Nice To Haves

Experience delivering enterprise-grade or global, scalable data-intensive or analytics-focused solutions and platforms.
Deep hands-on experience with Databricks (Delta tables, notebooks, jobs, workflows) and/or Spark for data engineering, analytics, or AI workloads.
Knowledge of relational and dimensional data modeling, data quality practices, metadata management, and data governance in an enterprise environment.
Demonstrated experience designing and implementing agent and skill ecosystems, including: Defining and maintaining SKILL.md-style definitions and associated Markdown assets . Wiring agents to MCP servers and external tools in a secure, governed way. Applying version control, testing, and rollout patterns to agents and skills.
Demonstrated ability to influence technical direction, establish reusable patterns and standards, and mentor less experienced engineers.
Ability to manage multiple initiatives and priorities in a fast-paced environment while maintaining high engineering standards.

Responsibilities

Design, develop, and maintain data-driven and AI-enabled data solutions and services that support Finance and Legal teams.
Develop high-quality, performant SQL and PySpark code in Databricks for complex data transformations and analytical data modeling.
Design, build, and optimize Databricks (DBX) data pipelines and workflows to support scalable batch and near real-time data processing.
Partner with data science and AI teams to productionize AI/ML and LLM-based solutions, including feature pipelines, inference integrations, monitoring, and continuous improvement.
Develop agents and automated workflows using Glean and Genie to transform Finance and Legal ways of working.
Proactively identify and remediate issues related to patterns, performance, security, and data correctness.
Lead or contribute to solution design, including architecture, patterns, and technology choices aligned with GM standards and Statement of Technical Direction.
Troubleshoot and resolve production issues across the full stack (data, application, infrastructure), driving root cause analysis, stable fixes, and clear documentation.
Document software and data solutions and ensure technical documentation meets GM standards and can be leveraged across FLSE and related business systems.