- Currently working as a Database Administrator | System Analyst, with 2.5+ years of enterprise SQL and analytics experience.
- Specializing in Data Analytics & Business Analytics, applying SQL, Power BI, and Python to deliver actionable insights that drive decisions.
- Passionate about building analytics solutions that transform raw data into business‑ready dashboards and decision frameworks.
- Experienced in large‑scale enterprise data & analytics projects:
- Customer Churn & Retention Analytics (RFM segmentation of 541K+ transactions)
- Retail Inventory & Sales Forecasting (AI seasonal forecasting across 4 years of retail data)
- Open to collaborating on Business Analytics & Open Source projects, especially those involving SQL optimization, BI dashboards, and forecasting models.
- 📫 Reach me at arjunmpec101@gmail.com | LinkedIn
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🗄️ Customer Churn & Retention Analytics (RFM Model)
Built an end-to-end analytics pipeline: Python (Pandas) for EDA and RFM aggregation of 541K+ transactions, hardened schema in SQL Server, and modeled in Power BI.- Engineered an RFM segmentation model using RANKX quintile scoring in DAX, dynamically assigning 1–5 scores for Recency, Frequency, and Monetary value.
- Produced four actionable customer segments: Champions, Loyal Customers, At Risk, and Hibernating — enabling targeted retention strategies.
- Developed an interactive What-If Revenue Recovery simulator using Power BI Numeric Range parameter + SELECTEDVALUE DAX.
- Enabled marketing stakeholders to model financial impact of retention campaigns in real time against the At-Risk segment.
- Delivered a business-ready churn dashboard combining raw data ingestion → RFM scoring → visualization → revenue recovery simulation.
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📊 Retail Inventory & Sales Forecasting
Engineered a multi-layer data pipeline: SQL Server View for raw abstraction → Power Query monthly aggregation (9,994 daily rows → 573 monthly rows) → Power BI Time Intelligence model.- Implemented CALENDARAUTO DateTable with SAMEPERIODLASTYEAR and TOTALYTD DAX measures for YoY and YTD benchmarking across 4 years of retail transactions.
- Built an AI-powered 3-month seasonal forecast (exponential smoothing, seasonality=12, 95% CI) with conditional alert cards that auto‑highlight declining categories.
- Designed an interactive Tooltip Page linked to forecast charts — hovering over spikes surfaces category-level breakdown instantly.
- Enabled operations managers to identify declining categories without scanning tables, accelerating decision-making.
- Delivered a business-ready forecasting solution integrating SQL + Power BI + AI forecasting for inventory planning and revenue optimization.
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🧱 Retail Sales SQL Data Warehouse
End‑to‑end SQL data warehouse implementing a Bronze → Silver → Gold layered architecture for retail sales.- Built entirely in SQL Server/MySQL (no external ETL tool)
- Bronze layer mirrors raw CRM & ERP source tables (customers, products, sales, locations)
- Silver layer applies data quality checks (ID normalization, date validation, gender/marital‑status standardization)
- Gold layer models a star schema with fact_sales, dim_customers, and dim_products using surrogate keys
- Uses window functions (ROW_NUMBER) and joins to integrate history, resolve conflicts, and conform dimensions
- Produces analytics‑ready views/tables suitable for BI tools and downstream reporting
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