Summary
Overview
Work History
Education
Skills
Websites
Timeline
Generic

Yeshwanth Reddy T

Glen Allen,VA

Summary

Results-driven Data Analyst with 5+ years of experience turning raw data into actionable insights for business strategy and process optimization. Strong expertise in SQL, Python, and BI tools (Tableau, Power BI) for analytics, reporting, and dashboard development. Strong expertise in SQL (CTEs, window functions, performance tuning) and Python for building end-to-end data pipelines, data transformation, and validation of structured and semi-structured datasets. Hands-on experience developing and supporting AWS-based data solutions to enable scalable analytics and reporting. Proficient in building interactive Tableau dashboards for executive leadership, delivering insights into KPIs, revenue trends, customer behavior, and operational performance. Skilled in building ETL pipelines, performing data modeling, and automating analytics workflows using AWS, Snowflake, and GCP. Strong collaborator with cross-functional teams, ensuring clear communication of data changes, impacts, and requirements.

Seasoned collaborator experienced in meeting needs, improving processes and exceeding requirements in team environments. Diligent worker with strong communication and task prioritization skills.

Overview

6
6
years of professional experience

Work History

Data Analyst

JP Morgan Chase and Co
03.2023 - Current
  • Delivered end-to-end data solutions for mortgage and credit analytics by building automated pipelines, ensuring data quality, and developing executive dashboards. Improved data accuracy, reporting speed, and decision-making across high-volume financial datasets.
  • Built Tableau dashboards and KPI scorecards tracking loan origination trends, credit risk exposure, and performance metrics for leadership reviews.
  • Built and optimized SQL queries on large customer and transactional datasets using CTEs, window functions, joins, and aggregations.
  • Performed data analysis using SQL to identify trends, anomalies, and performance gaps in customer behavior and revenue data.
  • Improved query performance through query refactoring, indexing, and efficient filtering strategies.
  • Developed reusable SQL views and tables to support reporting and downstream analytics.
  • Designed data pipelines in Databricks and AWS Glue to ingest and transform multi-source datasets, improving data availability and accuracy.
  • Implemented data governance and compliance controls including data lineage, validation rules, and audit-ready reporting to meet regulatory standards.
  • Performed SQL-based data quality checks and quality check queries including null checks, duplicate detection, referential integrity validation, and type mismatch audits.
  • Built SQL scripts to compare source vs. target tables using row counts, checksum validation, and reconciliation queries.
  • Built pipelines to migrate data from AWS S3 to Snowflake Data Warehouse and performed SQL operations for Data Quality checks and validation thus reduced missing values by 25%.
  • Partnered with data engineering and risk analytics teams to define key business metrics and ensure consistent data definitions.
  • Communicated upstream data changes, impacts, and risks to cross-functional stakeholders, enabling smooth platform updates and minimal disruption.
  • Environment: SQL, Python, Pandas, Tableau, Databricks, Snowflake, AWS (S3, Glue, Redshift, Lambda), Kafka, Git, Jira, Excel, Google Sheets.

Data Analyst

Cognizant Technology Solutions
07.2021 - 01.2023
  • Delivered automated AWS/Snowflake data pipelines and BI reporting solutions supporting healthcare operations, claims processing, member analytics, HR/payroll insights, and financial reporting. Enhanced data quality, operational efficiency, and decision-making through SQL/Python optimization and robust validation workflows.
  • Designed and developed ETL pipelines using AWS Glue and Lambda to automate ingestion, cleansing, and transformation of healthcare claims, eligibility, provider, and financial datasets.
  • Processed Workday HR and healthcare payroll data to support KPI reporting, workforce analytics, and compliance dashboards.
  • Improved SQL query performance by 40%+ through optimization and tuning across claims and member datasets.
  • Automated ingestion of EHR/Workday extracts, claims files, encounter data, and provider rosters into AWS/Snowflake using Python and Glue with built-in quality checks and error handling.
  • Conducted root-cause analysis for data mismatches across eligibility, claims adjudication systems, and downstream reporting layers, significantly improving accuracy.
  • Built clinical, operational, and financial dashboards in Power BI and QuickSight (claims cost trends, utilization metrics, provider performance, workforce metrics).
  • Improved query efficiency by 50% using SQL optimization and Snowflake partitioning, clustering, and caching strategies—resulting in faster claims and member-level analytics.
  • Supported migration of healthcare workloads from Teradata to Snowflake, ensuring HIPAA-compliant data handling and scalable processing.
  • Implemented data governance rules, quality checks, and validation scripts across member, claims, provider, and HR datasets.
  • Delivered ad-hoc healthcare insights (e.g., cost/utilization trends, workforce spend analysis, provider network health) to senior leadership for strategic planning.
  • Environment: SQL, Python, Snowflake, AWS Glue, Lambda, Redshift, Power BI, QuickSight, Jenkins, Git, Agile

Data Analyst

Genpact
03.2020 - 06.2021
  • Built dashboards and GCP-based data pipelines to support SLA, process KPI, and operational analytics. Improved data quality and reporting reliability through profiling, validation, and collaboration with business teams.
  • Conducted data profiling, cleaning, and analysis of operational datasets to identify process inefficiencies.
  • Built Tableau and Power BI dashboards to visualize SLA adherence, process KPIs, and business metrics.
  • Implemented data pipelines on GCP (BigQuery, Cloud Functions, Cloud Storage) to integrate and analyze large datasets.
  • Developed data validation frameworks in Python ensuring data accuracy before downstream use.
  • Partnered with business teams to define KPIs and deliver analytical insights that improved reporting accuracy.
  • Environment: SQL, Python, Tableau, Power BI, BigQuery, GCP Cloud Functions, Pandas

Education

Master of Sciences - Information Systems, Business Analytics

California State University Fullerton

Bachelor of Technology - Computer Science and Engineering

JNTU
Hyderabad

Skills

  • Data analysis with SQL and Python
  • Data visualization expertise: Tableau, Power BI, QuickSight, Excel (Pivot Tables, Power Query)
  • ETL process design
  • AWS services expertise: S3, Glue, Redshift, Lambda
  • Continuous integration and delivery
  • Banking expertise

Websites

Timeline

Data Analyst

JP Morgan Chase and Co
03.2023 - Current

Data Analyst

Cognizant Technology Solutions
07.2021 - 01.2023

Data Analyst

Genpact
03.2020 - 06.2021

Bachelor of Technology - Computer Science and Engineering

JNTU

Master of Sciences - Information Systems, Business Analytics

California State University Fullerton

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