Operations Data Analyst

USA Compression Partners, LP, (NYSE: USAC) provides mission-critical natural gas compression services to large upstream and midstream energy companies. We are an operations-centric, technology-driven employer with 800+ employees in 18 states across the US. We owe our success to the quality of our employees, our strong commitment to safety, and our superior service to our customers. Position Overview This is an opportunity to play a key role in a small, collaborative team that supports the full analytics lifecycle. You will partner with stakeholders to identify business needs and improvement opportunities, prepare and transform data, and develop, test, support, and deploy dashboards and applications, primarily through Databricks. The team works across financial, accounting, safety, fleet, and operational data, including SAP, to support USA Compression's operations leadership, improve processes, and drive value through data and technology. Key Responsibilities Business Analysis Partner with cross-functional managers, directors, and stakeholders to identify business needs, opportunities, and process improvements. Turn requirements into features, user stories, acceptance criteria, and data needs. Design dashboard concepts, review prototypes, and incorporate feedback. Explain technical options, risks, and dependencies in business terms. Development Onboard new data sources and connect through APIs or other ingestion methods using ETL/ELT processes. Use Python, PySpark, and SQL in Databricks to clean, validate, join, and transform data. Build Bronze, Silver, and Gold data layers. Develop dashboards and low-code internal applications. Create maintainable code and documentation using team standards. Quality Assurance Test data transformations, business rules, dashboards, and applications. Perform UI testing for dashboards and applications, including layout, navigation, controls, filters, and user interactions. Validate counts, joins, calculations, filters, edge cases, and source-to-target results. Perform regression testing and confirm acceptance criteria before release. CI/CD and Deployment Prepare and deploy notebook, data, dashboard, and application changes. Validate releases across environments and complete post-deployment checks. Document issues and support rollback or remediation when needed. Support Liaise with operations team as needed to facilitate dashboards, applications, and data questions. Troubleshoot data issues, identify root causes, and communicate resolutions. Document recurring issues and recommend improvements. Planning and Backlog Participate in an Agile development process, including sprint planning, backlog refinement, daily stand-ups, reviews, and retrospectives. Keep future work prioritized and ready for development and testing. Required Qualifications Bachelor's degree in Business Administration, Information Systems, Computer Science, Mathematics, or equivalent experience.2+ years of experience in data analytics, business intelligence, or a similar role. Skilled in developing dashboards, reports, or other data visualizations. Ability to gather requirements and translate them into deliverable features. Proficiency in testing data, dashboards, or applications. Strong written and verbal communication skills, including communication of progress reports, risks, blockers, and decisions. Detail-oriented and focused on accurate, high-quality work. Able to work independently and deliver accurate, reliable solutions. Follows development, documentation, testing, and deployment standards. Ability to collaborate closely with the team. Preferred Qualifications Python, PySpark, SQL, Databricks, or similar technologies. Knowledge of data transformation and medallion architecture. Familiarity with source control, CI/CD, and release practices. Knowledge of financial, accounting, safety, vehicle and fleet, or operational data. Familiarity with SAP-sourced data or low-code platforms. Knowledge of energy, industrial, or field-service analytics. Identify opportunities for process optimization and continuous improvement through data, automation, and technology. Ability and willingness to identify opportunities to leverage AI to improve analytics, processes, and decision-making.