Data Analyst Supply Chain

Veerasibi Veerapandian

DATA ANALYST

California, USA +1-669-***-**** **********@*****.*** LinkedIn SUMMARY

• Experienced Data Analyst with 5+ years of expertise in transforming complex datasets into actionable insights across healthcare, finance, supply chain, SaaS, and CRM domains, supporting both operational and strategic business decisions.

• Proficient in building scalable data pipelines and designing intuitive dashboards using tools such as Power BI, Tableau, and Looker to support cross-functional reporting and business intelligence initiatives.

• Skilled in applying statistical techniques, cohort analysis, A/B testing, and predictive modeling to uncover insights, improve operational efficiency, and support data-driven decision-making.

• Hands-on experience working with modern data platforms and cloud environments including AWS (S3, Redshift, Glue), Snowflake, and dbt for large-scale data management and automation.

• Adept at translating complex data into clear business narratives through data storytelling, executive reporting, and stakeholder collaboration within Agile and cross-functional teams. SKILLS

Methodologies: SDLC, Agile, Waterfall

Data Analysis: Exploratory Data Analysis (EDA), Descriptive & Inferential Statistics, Hypothesis Testing, ANOVA, Regression Analysis, Statistical Analysis, Data Mining, Data Cleaning, ETL Processes, Quantitative Analysis, A/B Testing, Cohort Analysis, Funnel Analysis Programming Language: Python, SQL, R

Packages: NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, TensorFlow, Seaborn, dplyr, ggplot2 Data Visualization: Tableau, Power BI, Looker, Excel Dashboards, Advanced Excel (VLOOKUP, Pivot Tables, Macros, Power Query), SRSS

IDEs: Visual Studio Code, PyCharm, Jupyter Notebook, IntelliJ Database: SQL Server, MySQL, PostgreSQL, Oracle, Snowflake, MongoDB Cloud Platform: AWS (RDS, S3, Glue, Lambda, EC2), Snowflake, Databricks, Google Cloud Platform Other Technical Skills: SSIS, SAS, Alteryx, Machine Learning Algorithms, Spark, Apache Airflow, dbt, CRM Systems

(HubSpot, Freshworks), Data Wrangling, Data Cleaning, Data Transformation, Data Warehousing, Data Lakes, Data Storytelling, Executive Presentations, Technical Documentation, Cross-functional Collaboration, KPI Development, Metric Definition, Business Requirements Analysis, Executive Reporting, Requirements Gathering, Data Storytelling, HIPAA, SOX, GDPR (basic understanding), Data Governance, Data Privacy, Healthcare Analytics, Financial Reporting, Insurance Analytics, Sales

& Marketing Analytics, JIRA, Confluence, Trello, Git, Github Soft Skills: Time Management, Leadership, Strategy Planning, Problem-Solving, Negotiation, Decision-Making, Documentation and Presentation, Analytical Thinking, Attention to Detail, verbal and written communication

EDUCATION

Master of Science in Master of Science in Information Systems - Santa Clara University, California, USA Bachelor of Engineering in Electronics and Communication Engineering - Thiagarajar College of Engineering, India WORK EXPERIENCE

Cigna California, USA

Data Analyst Jan 2024 – Present

• Extracted and transformed over 8 million healthcare records from AWS Redshift into structured analytics datasets using SQL and dbt, supporting outreach initiatives across high-risk member populations.

• Engineered risk-related features in Python to identify care gaps, utilization trends, and chronic condition indicators, enabling data- driven segmentation for personalized care coordination.

• Conducted in-depth cost analysis of inpatient stays, ER utilization, and prescription claims using SQL and Excel, informing revised intervention strategies that contributed to a $5.2M reduction in avoidable medical expenses.

• Developed a cohort-based segmentation model using Python (Pandas) to classify members by clinical risk and utilization behavior, resulting in a 22% improvement in care team engagement outcomes.

• Created and maintained Tableau dashboards with live connections to Snowflake, enabling clinical managers to track member outreach metrics, risk tier shifts, and care team workload in real time, reducing manual reporting by 35%.

• Automated monthly reports on member movement and program performance using Python and Excel Power Query, reducing report turnaround time and saving over 60 hours of team effort per month.

• Designed and executed impact assessments of care interventions using A/B testing and pre-post analysis in Python, which revealed an 11% decrease in ER visits and 13.6% lower costs in targeted segments.

• Partnered with cross-functional teams in Agile sprints to convert business questions into analytical deliverables, contributing to the successful launch of three new outreach programs across multiple regions. Deloitte Bengaluru, India

Data Analyst II Nov 2020 – Aug 2022

• Consolidated over 120 million financial records from five entities by developing data pipelines in AWS Glue and Snowflake, ensuring standardized schema and improving data ingestion accuracy to 98.5%.

• Built automated ETL workflows to ingest and transform general ledger, accounts payable, and accounts receivable data, reducing manual integration time by 70% and enabling daily financial data refreshes.

• Created SQL transformation scripts in Snowflake to align over 2,500 GL codes across entities under a unified chart of accounts, facilitating consolidated reporting and comparative financial analysis.

• Developed Excel-based forecasting models using Power Query, PivotTables, and VBA, enabling the finance team to assess multiple M&A scenarios including cost synergies, tax implications, and revenue projections supporting a $1.4B acquisition decision.

• Conducted trend analysis on OPEX and CAPEX using Python (pandas, NumPy), uncovering $18M in overlapping costs, which informed restructuring plans and post-merger budget alignment.

• Designed Power BI dashboards to visualize real-time financial KPIs (EBITDA, margin, working capital), reducing executive reporting time by 35% and improving stakeholder visibility.

• Deployed AWS Lambda functions for automated validation checks, identifying over 20,000 data anomalies in historical ledgers and ensuring compliance with SOX and SEC reporting standards.

• Maintained detailed documentation of data lineage and transformation logic in AWS Glue Data Catalog, supporting audit readiness and facilitating transparent integration reviews.

• Collaborated with finance, audit, and M&A advisory teams to reconcile financial statements across merging entities, meeting integration milestones within the targeted 90-day window.

• Mentored a team of six analysts on data modeling techniques and reporting standards, increasing efficiency in deliverables by 40% and ensuring consistent analytics across the integration lifecycle. Cognizant Bengaluru, India

Data Analyst Jul 2018 – Oct 2020

• Unified over 15 million records from Salesforce, Zendesk, and Google Analytics using optimized SQL (PostgreSQL) queries to build a centralized dataset for churn analysis, enhancing data reliability and streamlining data preparation workflows.

• Developed key behavioral metrics such as session inactivity, ticket volume, and delayed responses using Python (Pandas), enabling more accurate customer profiling and churn signal detection.

• Performed detailed exploratory analysis using Seaborn and Matplotlib to uncover correlations between feature usage patterns and customer attrition, directly guiding model design decisions.

• Partnered with the data science team to support churn model development in Scikit-learn (XGBoost, Logistic Regression), delivering a model with 0.89 AUC-ROC and enabling proactive targeting of high-risk customers.

• Built interactive Power BI dashboards to track churn rates, product engagement, and account-level risk scores, supporting data-driven interventions that improved customer retention by 22% in two quarters.

• Applied RFM segmentation and K-Means clustering in Python to group 500K+ customers into behavioral cohorts, enabling targeted outreach campaigns that increased re-engagement by 31%.

• Monitored model performance against actual churn over multiple quarters, identifying drift in predictive accuracy and initiating retraining efforts that improved precision by 14%.

• Delivered retention insights and cohort analysis during executive reviews, translating model outputs into actionable business strategies that influenced roadmap decisions and reduced voluntary churn. Projects

WinWinLabs — Marketing Analytics Platform Feb 2025 – Present

• Developed a marketing analytics platform that consolidated data from GA4, Google Ads, Meta Ads, and Salesforce CRM into BigQuery, enabling unified reporting and data-driven campaign optimization.

• Automated data ingestion and transformation pipelines using Apache Airflow, PySpark, Pub/Sub, and Cloud Functions, reducing processing time by 35% and minimizing manual intervention.

• Optimized data models for GA4 and Salesforce event logs, decreasing dashboard latency and lowering BigQuery query costs by 15%. Created Looker dashboards to track key marketing and financial metrics, improving ad spend allocation and return on investment. Tech Stack Used: BigQuery, Apache Airflow, PySpark, Cloud Functions, Looker, Python, GA4, Google Ads, Meta Ads, Salesforce Hypergrowth Labs — Marketing Funnel Analytics & Lead Data Pipeline Oct 2024 – Dec 2024

• Developed AWS Glue (PySpark) and Lambda ETL pipelines to consolidate prospect and engagement data from HubSpot, Apollo, and Clay into Amazon Redshift, creating a centralized data source for real-time marketing analysis.

• Designed Amazon QuickSight dashboards to visualize funnel performance, lead segmentation, and conversion metrics, improving reporting speed by 95% and enabling data-driven campaign adjustments.

• Performed in-depth funnel analysis using SQL and Python, identifying optimization opportunities that led to a 28% increase in lead-to- customer conversion rates through improved targeting strategies. Tech Stack Used: AWS (Glue, Lambda, Redshift), SQL, Python, QuickSight

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