Vamsi Gajjala
Data Analyst • Rocky Hill, Connecticut • v**************@gmail.com • 203****222 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Data Analyst with 3+ years of experience designing and delivering analytics, BI, and ETL solutions using Power BI, SQL Server, Python, and Azure Data Factory. Experienced in building governed semantic layers and dimensional models to enable self-service reporting for 40+ business users and rebuilding 12 executive dashboards. Built production classification and time‑series models on 2M+ records to support retention and demand planning, and automated ETL using ADF and Databricks to improve pipeline reliability. Strong background in DAX-driven semantic layer design, incremental ETL, PySpark transformations, and data quality/lineage practices. Comfortable partnering with product, finance and operations in Agile sprints to translate business questions into prioritized analytics deliverables and actionable insights.
Technical Skills
Programming Language: Python,SQL
Databases: MySQL
Cloud Platforms: Azure Blob Storage,Azure Active Directory
Data Engineering & Processing: PySpark
Data Warehousing: Dimensional Modeling,Data Warehousing
Data Analysis & Visualization: Pandas,DAX,Power BI,Tableau,Documentation
Machine Learning & AI: Classification,Clustering
AI/ML Frameworks & Libraries: scikit-learn
MLOps: Feature Engineering
Data Integration & ETL: Azure Data Factory,ETL,Incremental Loads
Compliance & Governance: Data Quality,RBAC
Project Management & Collaboration: Agile,Scrum
Work Experience
EXL
Data Analyst
Jan 2025
Delivered analytics and enterprise reporting improvements supporting revenue, retention, and executive decision-making through BI, ML, and cloud ETL.
Tech Stack: Power BI, SQL Server, Python, scikit-learn, Azure Data Factory, PySpark, Azure Active Directory
- Rebuilt 12 Power BI dashboards on a governed SQL Server semantic layer, reducing executive reporting turnaround from 5 days to under 8 hours and enabling 40+ business users to self-serve revenue and retention KPIs.
- Developed a Python classification model using scikit-learn on 2M+ customer records to rank retention risk and deliver weekly target lists to CRM, contributing to an 18% reduction in churn among high‑risk accounts.
- Re-architected 20+ Azure Data Factory pipelines to implement incremental loads and partitioned PySpark transformations, improving ETL reliability and maintainability.
- Consolidated 30+ ad-hoc reports into a certified star-schema data model in SQL Server with documented KPI definitions and a shared data dictionary, eliminating a majority of recurring reporting discrepancies.
- Implemented RBAC and row-level security across Power BI workspaces using Azure Active Directory groups to meet internal audit and compliance requirements on first review.
- Partnered with product, finance, and operations in Agile sprints to shorten analytics delivery by translating business questions into prioritized Power BI work and well-defined acceptance criteria.
Tech Mahindra
Associate Data Analyst
Feb 2022 – Dec 2023
Supported forecasting, planning and enterprise reporting by integrating transactional sources and building analytics for sales, operations and demand planning.
Tech Stack: Python, scikit-learn, Azure Data Factory, Power BI, DAX, Tableau
- Built time-series and regression models using Python and scikit-learn on three years of transactional history, improving demand forecast accuracy by 22%.
- Automated recurring ETL workflows in Azure Data Factory and Databricks, eliminating approximately 35 manual reporting hours per month.
- Rewrote SQL queries and added covering indexes to reduce dashboard load time and replaced row-level calculations with pre-aggregated summary tables to improve performance.
- Established validation rules, lineage documentation, and exception-handling checks across production data feeds to improve data quality and reduce downstream errors.
- Designed a centralized Power BI semantic layer with standardized DAX measures for multiple business units to reduce cross-unit reporting discrepancies.
- Delivered 20+ deep-dive analyses on sales, customer, and operations data using SQL and Tableau, translating findings into recommendations adopted by leadership.
Tech Mahindra
Data Analyst Intern
May 2021 – Jan 2022
Supported sales and retention reporting, data cleaning, and cloud migration efforts for regional teams; built dashboards and segmentation analyses.
Tech Stack: MySQL, Power BI, Python, Pandas, scikit-learn, Azure Data Factory, Azure Blob Storage, SQL
- Authored MySQL queries to extract and aggregate sales and retention datasets, reducing manual report preparation effort by 25%.
- Built 5 Power BI dashboards tracking sales, retention, and service KPIs that were adopted by three regional teams for weekly performance reviews.
- Cleaned and standardized 500K+ records in Python using Pandas to resolve duplicates and nulls and improve downstream analysis reliability.
- Supported Azure Data Factory pipeline setup and Azure Blob Storage ingestion to migrate reporting datasets to the cloud and improve refresh reliability.
- Identified actionable customer segments by applying regression and k-means clustering in scikit-learn to inform targeted retention outreach.
- Improved ad-hoc analysis speed by writing parameterized SQL queries and developing reusable Power BI templates for regional analysts.
Education
Sacred Heart University
Master of Science, Computer Science • Mar 2025
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