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Med-National Staffing Inc. seeks a detail-oriented Data Analyst to support Field Service Operations. This hybrid role based in Melvindale, MI focuses on daily, weekly, and annual reporting, forecasting, and workload management to drive better business decisions.
The ideal candidate has a strong analytical mindset, experience with SQL and Power Platform tools, and a background in data science or related fields. This contract offers benefits and long-term potential for extension or direct hire.
Location: Hybrid (Melvindale, MI) Pay Rate: $29/hour This is a long term contract currently budgeted for 12-months. Possible extension or direct hire for the right candidate. Benefits available.
Our Client is seeking a detail-oriented and tech-savvy Data Analyst to support Field Service Operations within the EGPM Organization. This position plays a key role in managing daily, weekly, and annual data reporting, forecasting, and workload management activities. The ideal candidate will have strong analytical and database skills, along with a proactive mindset for improving processes and supporting business decisions through data.
Generate ad hoc and routine reports to support operational performance and decision-makingPerform data-driven analysis and project support, including data collection, review, and making recommendationsWrite and execute SQL queries to extract and manipulate dataDesign and present clear data visualizations to reflect trends and key metricsCoordinate with stakeholders to gather requirements for analysis, reports, and data solutionsMaintain and recommend improvements to existing data processes and systems
Bachelor’s degree in Data Science, Analytics, Computer Science, Information Systems, or Business AdministrationHands-on experience with Microsoft Power Platform tools (Power Apps, Power BI, Power Automate)Working knowledge of SQL Server and general database management principles
Familiarity with Microsoft Azure Data Factory and Azure Data Explorer ToolsAdvanced skills in Microsoft Excel, including formulas, pivot tables, and data modeling