Manjunath Edapanur
Professional Summary
Data Analyst with 0.5 years of experience building analytics solutions, dashboards and ETL flows using Power BI, SQL and Power Query. Experienced in consulting-style reporting and delivering insights for retail quick-commerce and OTT analytics projects. Proficient in Python (Pandas, NumPy, Matplotlib) for exploratory data analysis and transformation, and in MySQL and Microsoft SQL Server for relational data extraction. Delivered 10+ interactive Power BI dashboards with DAX measures and standardized KPI definitions; developed repeatable ETL patterns to improve dataset reliability. Skilled at translating business questions into KPI-driven visualizations and data models, creating reusable analytic artefacts, and presenting insights to stakeholders to inform product and revenue decisions.
Technical Skills
Work Experience
- Developed 10+ interactive Power BI dashboards using DAX and Power Query to track core KPIs for stakeholder reviews.
- Built and maintained ETL workflows with Power Query and SQL to consolidate sales and operations data from disparate sources.
- Validated dataset integrity by writing SQL quality checks and Excel-based reconciliation routines to identify and fix anomalies.
- Designed standardized data models and reusable DAX measures to accelerate dashboard delivery and ensure consistent KPI definitions.
- Performed exploratory trend and variance analysis to surface patterns in revenue and operational metrics that informed weekly reviews.
- Presented dashboard findings and recommended actions to business stakeholders to support decision-making and reporting cadence.
Projects
- Built an interactive Power BI dashboard analyzing 1,000+ sales records to surface outlet and category performance.
- Applied Power Query transformations to clean and standardize transactional data prior to modeling.
- Created DAX measures and a star data model to compute KPIs such as total sales, average sales and outlet contribution.
- Derived actionable insights on outlet size and city-tier performance that informed assortment and pricing discussions.
- Conducted end-to-end exploratory data analysis on 4,000+ OTT titles using SQL queries and Python to identify content trends.
- Transformed and cleaned datasets with Pandas and NumPy to prepare feature tables for analysis.
- Visualized genre popularity and release patterns using Matplotlib to communicate trends across time and regions.
- Versioned analysis scripts and hosted artifacts using Git and GitHub; authored and iterated code in Visual Studio Code.
Education
Certifications
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