Hi There,
I'm Deepak Vemula
Experienced
Resume
With over 3 years of experience as a data analyst, I help retail brands make smarter, faster decisions by turning complex data into meaningful, actionable insights.
I specialize in building dynamic dashboards, forecasting tools, and reporting automation using Power BI, SAP IBP, SQL, Excel, and Python. I’m passionate about creating decision-ready reports that align data strategy with business impact.
Currently, I work at Caleres, Inc., where I support brands like Famous Footwear, Vionic, and Naturalizer through performance tracking, demand planning, and executive-level reporting. I’ve also driven impact at Sodexo – Starbucks and Capgemini (Morgan Stanley) by improving inventory and benefits analytics.
Skilled in DAX, Tableau, ETL processes, and data modeling, I combine technical depth with a strong sense of business context to deliver clear, strategic insights across teams.
📊 My mission is simple: Turn messy data into clear answers that drive strategy and action.
University (Graduated) : Saint Louis University ( SLU )
email : vemuladeepak1999@gmail.com
place : St. Louis, USA
- Designed Power BI dashboards for sales and inventory across Caleres brands like Famous Footwear , Naturalizer and Vionic.
- Automated SAP IBP reporting, improving forecast accuracy and saving 15+ hours/month.
- Delivered weekly executive reports used by C-suite to guide strategic retail planning.
- Implemented an automated inventory system that reduced stockouts and overstock incidents by 20% through improved forecasting and reorder logic.
- Analyzed historical consumption trends using Excel (VLOOKUP, pivot tables), cutting excess stock by 25% and improving cash flow.
- Streamlined dashboard reporting cycles using lean analytics, reducing update time by 20% and accelerating decision-making.
- Created Tableau dashboards to visualize benefit utilization trends, improving plan targeting and increasing client satisfaction by 12%.
- Applied data cleansing and outlier detection techniques to healthcare datasets, boosting reporting accuracy by 25%.