The Systems Engineer - Data and Insights joins the Finance Data and Insights team within the Business Applications organization. In this role, you will develop data-intensive and AI-enabled solutions to support Finance and Legal teams by streamlining workflows and accelerating decision-making. You will design and maintain software, data pipelines, and automated workflows, focusing on data quality, performance, and reliability. Key responsibilities include developing high-quality SQL and PySpark code in Databricks for complex transformations and productionizing AI/ML and LLM-based solutions. You will utilize tools such as Databricks, Python, Glean, and Genie to build agents and skills. The work involves solving complex data requirements, managing cloud initiatives, and ensuring technical documentation meets standards while driving transformation in ways of working through automated processes.
What does a Systems Engineer earn?
Median $160000 from 251 postings across 41 companies.
Design, develop, and maintain data-driven and AI-enabled solutions for Finance and Legal teams.
Develop high-quality SQL and PySpark code in Databricks for complex data transformations and analytical modeling.
Build and optimize Databricks data pipelines to support scalable batch and near real-time data processing.
Productionize AI/ML and LLM-based solutions including feature pipelines, inference integrations, and monitoring.
Develop automated workflows and agents using Glean and Genie to transform business processes.
Troubleshoot and resolve production issues across the full stack while performing root cause analysis.
Document software and data solutions to ensure technical specifications meet corporate standards.
What we're looking for
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.
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 building or configuring agents in platforms like Glean, Genie, or IDE-based agents.
Familiarity with Model Context Protocol (MCP) or similar patterns for connecting agents to tools, APIs, and data sources.
Strong problem-solving skills with the ability to break down complex technical and data challenges into clear, actionable steps.
Excellent written and verbal communication skills with the ability to collaborate with both technical and non-technical stakeholders.