Sudharshni Balasubramaniyam
Data Analyst • s****************@gmail.com • 937****221 • drivetube.ai/•••••
Professional Summary
Data Analyst with 3+ years of experience combining software engineering and analytics to improve master data quality, build ETL pipelines, and deliver KPI reporting for supply chain and enterprise systems. Skilled in SQL, Python, data validation, MDM concepts (Material, BOM, Product, Vendor, Logistics), and designing scalable data solutions that enable operational decision-making.
Technical Skills
Programming Languages: Python
Databases: SQL,PostgreSQL,MySQL,MongoDB
Cloud and DevOps: AWS S3,AWS RDS,AWS Lambda
Testing: Data Quality,Data Validation Rules,Data Stewardship,Data Lifecycle Management
Data and Analytics: Material Master,Bill of Materials BOM,Product Data,Vendor Data,Logistics Data,Tableau,Power BI,ETL Pipelines,Data Modeling,Data Warehousing,SQLAlchemy
Tools and Methodologies: Git,Query Optimization,Database Indexing
ERP and Enterprise Systems: SAP S,4HANA concepts,MM module,Order Management,Supply Chain Data Flow
Business Intelligence and Reporting: KPI Dashboards,Operational Reporting
Work Experience
University of Dayton
Student Data Analyst
Jun 2024 – Dec 2025
Supported university analytics and master data health monitoring; worked on supply chain and operational master-data elements and reporting.
Tech Stack: Python, SQL, SQLAlchemy, PostgreSQL, Tableau, Power BI, Git, AWS S3
- Built and maintained data quality monitoring dashboards and KPI reports to measure master data health, completeness, and compliance across operational systems using Tableau and Power BI.
- Designed and implemented data validation rules and governance checks for product, item, and transactional data, reducing downstream data discrepancies and improving report reliability.
- Performed root cause analysis on integration errors and data anomalies, identifying upstream ETL and source-system gaps and recommending corrective actions to data owners.
- Optimized SQL queries and restructured reporting data models to improve dashboard responsiveness and lower average query execution times across key operational reports.
- Developed automated ETL pipelines in Python and SQLAlchemy to standardize, transform, and load enterprise data into PostgreSQL, enabling repeatable ingestion and traceability.
- Partnered with business and technical stakeholders to translate supply chain requirements into data models and KPI definitions, enabling operational teams to act on insights.
VVDN Technologies Pvt. Ltd.
Software Engineer
Jan 2022 – Aug 2023
Worked at an electronics product engineering and manufacturing services company; developed backend and data solutions to support enterprise reporting and integrations.
Tech Stack: SQL, MySQL, PostgreSQL, REST APIs, Git, Database Indexing, Query Optimization
- Developed and optimized SQL-based reporting and analytics solutions to support enterprise operations and data-driven decision-making across product and transaction datasets.
- Designed scalable relational schemas aligned with master data entities (products, transactions, users) to ensure consistency and simplified downstream reporting.
- Investigated and resolved data inconsistencies and system integration failures by performing root cause analysis and implementing validation and reconciliation checks.
- Built backend APIs and data integration layers to simulate ERP-style data exchanges, enabling system-to-system synchronization of master and transactional data.
- Reduced report generation time by 30% through query optimization, proper indexing strategies, and refactoring of slow-performing joins and aggregations.
- Collaborated with cross-functional teams to validate data accuracy, enforce data governance rules, and improve end-to-end data quality and operational continuity.
Projects
Coffee Shop Sales Analysis & Data Quality Monitoring
Tools Used: Python, SQL, SQLAlchemy, PostgreSQL, Tableau, ETL Pipelines, Data Validation
- Designed and implemented a scalable ETL pipeline to ingest and transform sales data from multiple sources into PostgreSQL using Python and SQLAlchemy.
- Developed data validation frameworks including consistency checks, anomaly detection, and completeness rules to ensure high-quality transactional data.
- Built interactive Tableau dashboards tracking daily sales, order volume, revenue trends, and product performance to support business operations.
- Modeled relational datasets representing orders, products, customers, and transactions to standardize analytics and reporting.
- Performed root cause analysis on missing/duplicate records and incorrect entries, implementing corrective rules and improving data accuracy.
Respiratory Cancer Survival Prediction – SEER Data Analysis
Tools Used: Python, Data Cleaning, Data Imputation, Preprocessing, Data Governance
- Processed and validated 500K+ patient records applying data cleaning, transformation, and imputation techniques to ensure dataset consistency.
- Developed and evaluated predictive models in Python to analyze survival outcomes and translate analytical outputs into actionable insights.
- Implemented structured preprocessing pipelines and validation checks to ensure reproducibility and reliability of results.
- Identified and resolved data inconsistencies and missing values through root cause analysis, improving dataset integrity for modeling.
- Applied data governance best practices and standardized documentation to maintain consistent handling of sensitive healthcare data.
Education
University of Dayton
M.S. Business Analytics • Sep 2023 – Dec 2025
Coursework: Data Management, Supply Chain Management, Business Intelligence
Anna University
B.S. Computer Science and Engineering
Coursework: Database Management Systems, Algorithms, Software Engineering
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