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Data Analyst with 3+ years in financial data analytics, risk detection and SQL expertise.
Data Analyst with 3.5 years of experience in data analytics, risk detection and financial data assurance helping clients detect, monitor and resolve data discrepancies across large-scale enterprise datasets through the use of SQL, ETL pipelines, and data visualization tools. Proven ability to independently analyze complex transactional datasets (2M+ records) to identify fraud patterns, anomalies and risk signals translating data insights into actionable business decisions and client-facing recommendations. Hands-on experience in advanced SQL including complex joins, CTEs, window functions, views, stored procedures and query performance tuning complemented by proficiency in Python (Pandas, NumPy), Power BI and Advanced Excel for data preparation, cleansing and analysis. Experienced in end-to-end financial data reconciliation, ETL pipeline design and data quality frameworks across multiple systems by working with global clients and cross-functional teams to deliver analytical solutions from strategy to execution.
MVSR Engineering College, Hyderabad
Bachelor of Engineering (BE) · Computer Science and Engineering (CSE)
August 1, 2017 – July 1, 2021
Covasant Technologies Private Limited
Data Analyst
January 1, 2023 – Present
Hyderābād, Telangana, India
Data Breach Notifications
January 1, 2023 – June 1, 2026
Worked on US-based hospital data containing sensitive Personally Identifiable Information (PII) and Protected Health Information (PHI), ensuring strict data handling in compliance with privacy requirements — demonstrating experience with sensitive data governance applicable to regulated industries. Received raw input data in CSV files, analyzed client requirements, and identified required PII and specific PHI columns to be captured for the deliverable. Loaded the extracted data into Microsoft SQL Server for further processing and transformation. Designed and implemented automated SQL stored procedures to perform systematic column-level data validation — including data type verification, format and length checks for sensitive fields such as Social Security Number (SSN), and identification and rectification of data shifts where row data had incorrectly shifted into adjacent columns — replacing manual checks and improving accuracy and efficiency. Performed thorough quality control checks on every table to ensure data accuracy and consistency before consolidation. Merged data across multiple tables into a single consolidated structure containing client-specified columns as per deliverable requirements. Exported the consolidated data into a Consolidated Data Excel file and delivered both the Final Consolidated Table and the Consolidated Data Excel file as the client deliverable to the US-based client. Mentored and guided junior team members on project workflows, data processes, and technical queries — serving as a subject matter expert and go-to resource for the team throughout the project.
Financial Data Reconciliation
January 1, 2023 – June 1, 2026
Client organization processes vehicle repossession orders on behalf of lending institutions, requiring accurate tracking of revenue and cost transactions across multiple independent systems. Objective was to establish a centralized, consistent, and reliable financial source of truth for all repossession-related revenue and cost transactions consolidated from three independent source systems (RDN, RecoveryConnect, and RepoSystems). Analyzed Accounts Payable (AP) and Accounts Receivable (AR) data consolidated in the enterprise data warehouse from three independent source systems. Designed and developed advanced SQL queries using complex joins, CTEs, and window functions to compare and validate financial records across systems. Implemented SQL-based fuzzy matching logic to reconcile invoices across disparate systems, identifying discrepancies between amounts paid and received. Investigated data inconsistencies and performed root cause analysis to trace and resolve mismatches across systems. Identified and formally flagged data risks and discrepancies to the client along with proposed solutions, ensuring transparent communication and collaborative resolution before implementation. Built Azure Data Factory (ADF) ETL pipelines to ingest, stage, and transform data into a Medallion architecture (Bronze, Silver, Gold layers) compliant data warehouse. Applied Slowly Changing Dimensions (SCD Type 1 & 2) to maintain both current and historical financial records for accurate reporting and auditing. Established validation frameworks to ensure data consistency and integrity across the entire reconciliation process. Enabled the client to realize approximately $2M in financial recovery within a single quarter through effective discrepancy identification and resolution.
Joy of Computing using Python
NPTEL
June 1, 2026 – Present
Databricks Certified Data Engineer Associate
Databricks
June 1, 2026 – Present
Microsoft Certified: Azure Fundamentals
Microsoft
June 1, 2026 – Present
Python Developer
NASSCOM
June 1, 2026 – Present
SQL (Intermediate)
HackerRank
June 1, 2026 – Present
SQL (Basic)
HackerRank
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's experience across diverse projects, including financial data reconciliation, fraud analytics, and sensitive data handling, indicates adaptability and a broad skill set. Their work with global clients and cross-functional teams suggests an ability to thrive in collaborative and diverse environments. The focus on delivering actionable insights and achieving tangible business outcomes (e.g., $2M financial recovery) aligns with a results-oriented culture. The continuous pursuit of certifications (Databricks, Azure, Python, SQL) demonstrates a commitment to continuous learning and professional development.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through root cause analysis and discrepancy resolution. Their experience in mentoring junior team members and collaborating with global clients indicates good teamwork and communication. The ability to work in Agile sprint environments and use Azure DevOps for version control suggests a structured and organized approach to project delivery. The candidate's proactive identification and flagging of data risks, along with proposed solutions, highlights a strong sense of ownership and accountability.