Rahul Behre
Senior Business Information Analyst at Ashland
Based in Hyderabad, India
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Seniority
Staff
Department
Information Technology
Location
Hyderabad
Industry
Chemical Manufacturing
Company size
5.9K
Contact information
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r•••••••@ashland.com
Phone
5 credits+91 ••• •••• ••••
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Background
About Rahul Behre
SKILLS: Power BI, DAX, QlikView, Qlik Sense, Dashboards, Python, Data Modeling, Snowflake, Data Warehousing, Data Architecture, AI & ML Concepts, Excel, MS O365/Nintex, MS SQL Server, Visual Studio, Data Structures, OOPS, Lotus Notes Domino, Lotus Notes Scripts and Formula Language, Lean Six Sigma Yellow Belt Certified, Oracle Certified Java Programmer. PROFESSIONAL SUMMARY: • 13 years of experience in IT industry in all aspects of Business intelligence applications with a strong background in Data Modeling, Data Warehousing/ Decision support systems and Business Intelligence (BI). • Expertise in Designing and Development of Business intelligence applications with Power BI, Power Automate and QlikView 12/11 as BI tool and DAX/Scripting, developing and designing Dashboards by extracting data from multiple sources (SAP R3, SAP BW, SQL Server, Flat files, Excel, QVDs, QVW etc.). • QlikView to PowerBI Migration. • Automated the Business Planning and Forecasting process by 80%. • Created the Manufacturing matrix which consolidates manufacturing spend, absorption, volume, forecasting and business plan, forecast trend, volume trend from 23 different sources. This saved more than 70% of analysis time. • Automated SQL dataset preparation and created Sales GP report which summarizes the different aspect of the Sales data. This decreased the refresh time by 30%. • Global innovation data automation provides an accuracy of more than 95%. 10 hours of manual work is getting published in less than 5 minutes. • Consolidated and automated logistics dataset for Actual, Forecast, Business Plan and Last Year using SQL server stored procedures. This provides a detailed picture of the data which saved more than 40% of analysis efforts. • Increased the data granularity based on % allocation method using Plan and Forecast numbers.
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