Robert Half Finance & Accounting Business Analyst for West side Manufacturer in Strongsville Area, Ohio

A Strongsville area Manufacturer is looking for a Business Analyst. The Main responsibilities will include: - Utilizing Business Intelligence software to verify accuracy of the master database and financial data - Provides meaningful reporting and analysis to drive profitable top line growth. - Develops financial reports for forecasting, trending, and results analysis and performs such analysis - Interprets financial data to determine past performance and/or to project the financial probability - Assists in development and/or adjustments to budget or latest view volume / revenue - Extracts financial data from various ERP systems (including accounting and information systems) and and databases and performs financial analyses - Records, classifies and summarizes financial transactions/events in accordance with valid accounting principles to make recommendations to improve business results For Immediate CONFIDENTIAL Consideration, please e-mail your resume to or call Tom @ 216-621-4253

Robert Half Finance & Accounting is the world's leader in specialized financial staffing. We provide exciting full-time opportunities in the areas of accounting, bookkeeping, finance, audit, taxation and more. We pioneered the professional staffing industry, and we've been successfully matching professionals with employers since 1948. Our proven proprietary processes, along with our relationships in more than 340 locations worldwide, allow us to provide you unparalleled access to exciting career opportunities.

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Req ID: 03340-0008842358

Functional Role: Business Analyst

Country: USA

State: OH

City: Strongsville Area

Postal Code: 44136

Compensation: $70,000.00 to $80,000.00 per year

Requirements: - Bachelors degree in finance / business - +2 years related experience - Expert Excel skills - SAP highly preferred - Requires the ability to take large volumes of data and turn it into meaningful analysis – - Requires the ability to interpret ambiguous data into a meaningful analysis