Data Analytics Leader with 6+ Years Experience
Resume:
Vaishnavi Sanjana Karri
*****.******@*****.***
https://www.linkedin.com/in/vskarri/
Personal Summary
Data Analyst with 6+ years of experience delivering end-to-end analytics and business intelligence
solutions across telecom, healthcare, and financial services. Strong understanding of business processes and
Software Development Life Cycle (SDLC), including requirements gathering, functional documentation, UAT,
and production support. Expert in SQL, Python, Tableau/Power BI, ERP reporting systems, and enterprise data
platforms. Proven ability to translate business needs into scalable technical solutions and actionable insights.
Certifications
Tableau Desktop Certification
Technical Skills
● Data Management: SQL, MySQL, PostgreSQL,Snowflake,
● Analytics: Data Governance, ETL Design & Data Integration, Data Modeling & Dimensional Modeling
● Data Visualization & Reporting: Tableau, Power BI, Microsoft Excel (Advanced)
● Statistical Analysis: R, Statistical Modeling, Predictive Analytics, Forecasting & Time-Series Analysis,
What-If / Impact Analysis, Multivariate Statistical Analysis, A/B Testing, Root Cause Analysis
● Cloud Platforms: AWS, Azure, Snowflake, Databricks
● DBHDS: WAMS, CONNECT
● ERP & Business Systems: SAP BusinessObjects (WebI, Lumira, Analysis), ERP Reporting & Analytics,
Financial & Healthcare ERP Data Integration, Enterprise Data Warehousing
● Programming Languages: Java, Python, R
● Other Tools: Jupyter Notebook, Git, JIRA, Data Workflow Documentation
Areas of Expertise
● Developing and optimizing ETL pipelines using Python and AWS to process large-scale datasets
efficiently. Expertise in transforming raw data into actionable insights for business stakeholders.
● Creating interactive dashboards and reports using Tableau and Power BI to visualize complex data
trends. Skilled in designing user-friendly visualizations that drive strategic decisions.
● Writing complex SQL queries to extract and manipulate data from relational databases and data
warehouses. Proficient in optimizing queries for performance and scalability.
● Analyzing and modeling data to uncover trends, patterns, and anomalies in large datasets. Experienced
in applying statistical techniques to support business objectives.
● Implementing data warehousing solutions using Snowflake and Databricks for efficient data storage
and retrieval. Knowledgeable in designing data marts for specific business needs.
● Communicating technical findings through clear reports, presentations, and documentation. Strong
verbal and written skills tailored to diverse audiences.
● Own data quality, accuracy, and integrity for analytical datasets used across business units.
● Define data standards, validation rules, lineage, and documentation to ensure consistent enterprise
usage.
● Monitor critical data elements and implement proactive cleansing and anomaly-detection processes.
● Partner with engineering and business teams on data model design, integration patterns, and
source-to-target mappings.
Education
Boston University, Boston, MA Dec 2022
Master of Science in Computer Information Systems
Professional Experience
AT&T Labs Inc
Data Analyst Dec 2023 – present
Project: Build a real-time monitoring system to track telecom servers, predict failures, and reduce downtime
by 25%.
● Conducted kickoff meetings with network operations and DevOps teams to gather 47 functional
requirements.
● Documented use cases covering server uptime, CPU/memory spikes, and disk I/O thresholds.
● Designed process flows in Visio illustrating data flow from server agents Kafka Python ingestion
Power BI.
● Wrote Python scripts to collect telemetry data from Linux servers using SSH and REST APIs.
● Parsed JSON logs and flattened nested structures into tabular format using Pandas.
● Stored raw data in AWS S3 and created partitioned Glue catalogs for efficient querying.
● Built 18 complex SQL queries with window functions to calculate rolling 7-day averages of resource
utilization.
● Created materialized views in AWS Redshift to pre-aggregate metrics and reduce dashboard load time
● Developed Power BI dashboards with real-time streaming using DirectQuery and push datasets.
● Implemented DAX measures for anomaly detection (Z-score > 3) and failure prediction using historical
trends.
● Gathered and documented business requirements, functional specifications, and use cases.
● Supported ERP reporting using SAP BusinessObjects for finance and operations teams.
● Integrated ERP-source data into Snowflake/Redshift for enterprise analytics.
● Built Tableau dashboards on top of ERP financial and operational datasets.
● Translated business needs into technical specifications for data engineers and developers.
● Performed gap analysis between current-state and future-state processes.
● Created process flow diagrams and data flow diagrams.
● Supported UAT, defect triage, and sign-off.
● Conducted forecasting and what-if analysis on historical telemetry data to predict capacity risks and
failure scenarios.
● Translated analytical findings into business recommendations that reduced downtime by 25% and
false alerts by 40%.
● Developed self-service dashboards and trained analysts to independently explore performance trends
and root causes.
● Partnered with stakeholders to ensure business process consistency and integration across
monitoring, incident, and ticketing systems.
Conducted data source and system reviews of server telemetry platforms to assess data
completeness, accuracy, and reliability.
● Evaluated existing data pipelines and source systems to identify threats to data validity (missing
values, inconsistent timestamps, duplicate records, schema drift).
● Developed actionable recommendations to improve data reliability, standardization, and monitoring.
● Conducted stakeholder and subject-matter expert interviews with network operations, DevOps, and
IT teams to understand source system usage and pain points.
● Reviewed system training materials and shadowed users to understand operational workflows.
● Documented business processes and source-to-target mappings.
● Integrated Jira tickets into Power BI via REST API to correlate incidents with server events.
● Used JavaScript custom visuals to display network topology and real-time latency heatmaps.
● Wrote Bash scripts to automate data validation and trigger alerts via Slack webhooks.
● Reduced false positives in alerts by 40% through refined thresholding logic in Python.
● Trained 15 operations analysts on dashboard navigation and drill-down capabilities.
● Led bi-weekly sprint reviews and backlog grooming sessions in Jira.
● Created ad-hoc SQL reports for FCC compliance on network availability (99.99% uptime).
● Optimized Redshift clusters by implementing sort keys and distribution styles, saving $12K annually.
● Documented data lineage from source to dashboard in Confluence for audit purposes.
● Used Git for version control of Python scripts and Power BI .pbix files.
● Facilitated UAT sessions with 30+ users and incorporated 92% of feedback within two sprints.
● Ensured SOC 2 compliance by masking PII in logs and encrypting data at rest.
● Handed over knowledge and runbooks to the support team before project closure.
NeenOpal
Data Analyst Jan 2023 – Nov 2023
Project: Develop a centralized analytics platform using IQVIA claims and prescription data to enable 30%
faster market insights for pharmaceutical clients.
● Facilitated 14 requirement workshops with product managers and data scientists to define 62 KPIs.
● Authored functional specifications and detailed use cases for patient journey and drug adherence.
● Mapped data flow from IQVIA SFTP BigQuery Snowflake Tableau using Visio diagrams.
● Wrote Python scripts to ingest monthly claims data via GCP Cloud Functions.
● Cleaned and standardized 180+ diagnosis codes using ICD-10 lookup tables in Pandas.
● Created 22 partitioned tables in BigQuery to enable time-series analysis by month and region.
● Built 31 materialized views in Snowflake to pre-join claims, prescriptions, and provider data.
● Reduced query runtime from 2.5 minutes to 9 seconds through view optimization and clustering keys.
● Designed Tableau dashboards with LOD calculations for cohort retention and treatment persistence.
● Implemented row-level security in Tableau to restrict data by client and geography.
● Performed predictive and cohort analysis on patient behavior, adherence, and persistence.
● Delivered executive-level presentations translating statistical results into product and market
recommendations.
● Built self-service Tableau workbooks and trained business users on filters, parameters, and
drill-downs.
● Established data quality rules, reconciliation checks, and audit trails for regulatory and client reporting.
● Developed interactive filters for drug class, HCP specialty, and payer type with dynamic parameters.
● Automated data refresh using Tableau Server schedules and Snowflake streams.
● Performed data source assessments on IQVIA claims and prescription systems to evaluate data
quality, completeness, and consistency.
● Conducted data validity and reliability analysis for patient, provider, and payer datasets used in
regulatory and client reporting.
● Reviewed and synthesized data quality issues and prioritized remediation actions.
● Developed data dictionaries and system documentation to improve data understanding and usage.
● Integrated Jira epics into Tableau via custom SQL to track data delivery SLAs.
● Wrote complex SQL with window functions to calculate market share and patient persistence rates.
● Created ad-hoc reports for FDA submissions using Tableau extracts and PDF exports.
● Used AWS Redshift for cost-effective archival of historical data (>3 years).
● Built Python ETL jobs to detect data anomalies (e.g., duplicate NPI numbers) and log in Jira.
● Presented insights to C-suite clients, resulting in 3 new product launches.
● Led data governance council to define PII redaction rules compliant with HIPAA.
● Trained 18 business users on self-service analytics via Tableau workbooks.
● Used GitHub Actions to automate testing of SQL and Python code before production deployment.
● Reduced dashboard development cycle from 6 weeks to 2 weeks using reusable components.
● Created Confluence pages with data dictionary, glossary, and dashboard user guides.
● Facilitated sprint planning and daily standups in Jira for a 6-member analytics team.
● Delivered final project deck with ROI: $2.3M in new client contracts from accelerated insights.
● Ensured 100% audit trail by logging all data transformations in Snowflake query history.
MassMutual
Data Analyst Sept 2021 – Dec 2022
Project: Analyze financial data to support investment and risk management strategies for insurance products.
● Analyzed financial datasets to identify investment trends, improving portfolio performance by 10%.
Collaborated with portfolio managers to implement data-driven strategies.
● Developed Tableau dashboards to visualize financial metrics, enabling real-time monitoring of
investments. These dashboards reduced reporting time by 20% for finance teams.
● Wrote SQL queries to extract data from Oracle databases, ensuring accurate financial reporting.
Optimized queries to handle large datasets, improving performance by 25%.
● Conducted data quality assessments on financial datasets, identifying inconsistencies in 12% of records.
Implemented cleaning processes to enhance data reliability for reporting.
● Utilized Python to automate ETL processes for financial data, reducing manual processing time by 30%.
These scripts ensured seamless data integration for reporting.
● Created SAP BusinessObjects reports to support financial forecasting, streamlining data delivery. These
reports improved forecasting accuracy by 15% for investment teams.
● Supported ERP reporting using SAP BusinessObjects for finance and operations teams.
● Integrated ERP-source data into Snowflake/Redshift for enterprise analytics.
● Built Tableau dashboards on top of ERP financial and operational datasets.
● Developed data models to support predictive analytics for investment returns, achieving 90% accuracy.
These models optimized resource allocation for portfolios.
● Conducted multivariate statistical analysis to evaluate risk drivers and investment performance.
● Developed predictive models supporting investment return forecasting and portfolio optimization.
Served as data owner for financial reporting datasets used by portfolio and risk teams.
● Delivered business recommendations influencing asset allocation and risk mitigation strategies.
● Collaborated with finance teams to gather requirements for new reports, ensuring alignment with
business goals. Delivered customized reports that improved decision-making by 12%.
● Optimized database schemas for financial transaction data, improving query response times by 20%.
These schemas enhanced scalability for future data growth.
● Leveraged AWS Redshift for financial data storage and analysis, optimizing data workflows. This
optimization reduced data access times by 15% for analytical processes.
● Conducted A/B testing analysis for insurance product campaigns, identifying high-impact strategies.
This analysis improved campaign ROI by 8%.
● Developed XML-based ETL processes to integrate financial data sources, ensuring system compatibility.
This integration improved data accessibility for finance teams.
● Created Python scripts for automated data validation, ensuring 98% accuracy in financial reports. These
scripts minimized errors in critical deliverables.
● Documented data workflows, creating a knowledge base for finance team reference. This
documentation reduced onboarding time for new analysts by 10%.
● Utilized JavaScript to enhance web-based financial report interactivity, improving user engagement.
These enhancements increased report adoption by 15%.
● Evaluated third-party data sources for financial analytics, expanding data coverage by 20%. This
expansion improved the accuracy of investment forecasts.
● Trained finance team members on Tableau best practices, enhancing visualization capabilities. This
training improved dashboard development efficiency by 18%.
● Monitored data system performance using AWS CloudWatch, identifying and resolving bottlenecks. This
monitoring improved system uptime by 10%.
● Presented financial insights to senior management, driving strategic investment decisions. These
insights contributed to a 5% increase in portfolio returns.
● Developed reusable SQL templates for recurring financial reports, reducing development time by 20%.
These templates standardized reporting across finance teams.
● Conducted system and data quality reviews of financial source systems supporting investment and
risk reporting.
● Assessed reliability of data used for regulatory and compliance reporting.
● Identified risks in upstream data feeds and recommended corrective actions.
● Optimized data segmentation for customer profiling, improving targeting for insurance products. These
efforts increased customer response rates by 10%.
● Integrated Google Cloud BigQuery for ad-hoc financial analysis, reducing query times by 15%. This
integration supported rapid decision-making for investments.
● Performed root cause analysis on financial data discrepancies, resolving 90% of issues within 24 hours.
This rapid resolution ensured timely delivery of reports.
● Created automated data quality checks using Python, ensuring high reliability in financial deliverables.
These checks reduced errors by 12% in financial reports.
Infosys Ltd
Operations Executive Engineer - Data and Analytics Team Nov 2019 – Aug 2021
Project: Support internal tool development and healthcare data analysis for operational efficiency.
● Analyzed healthcare datasets to identify operational inefficiencies, improving process workflows by
12%. Collaborated with internal teams to implement data-driven solutions.
● Developed Tableau dashboards to visualize operational metrics, enabling real-time monitoring. These
dashboards reduced reporting time by 18% for internal stakeholders.
● Wrote SQL queries to extract data from MySQL databases, ensuring accurate data retrieval. Optimized
queries to improve performance by 20% for large datasets.
● Conducted data quality assessments on internal datasets, identifying inconsistencies in 10% of records.
Implemented cleaning processes to enhance data reliability for reporting.
● Utilized Python to automate ETL processes for internal tools, reducing manual processing time by 25%.
These scripts ensured seamless data integration for analytics.
● Created SAP BusinessObjects reports to support operational forecasting, streamlining data delivery.
These reports improved forecasting accuracy by 10% for internal teams.
● Performed statistical analysis using SAS to evaluate operational trends, identifying key insights. These
insights drove process optimization strategies for internal tools.
● Developed data models to support predictive analytics for resource allocation, achieving 90% accuracy.
These models optimized operational efficiency for healthcare projects.
● Collaborated with internal teams to gather requirements for new reports, ensuring alignment with
project goals. Delivered customized reports that improved decision-making by 10%.
● Optimized database schemas for operational data, improving query response times by 15%. These
schemas enhanced scalability for future data growth.
● Leveraged AWS S3 for data storage and retrieval, optimizing data workflows for internal tools. This
optimization reduced data access times by 12% for analytical processes.
● Conducted A/B testing analysis for internal tool performance, identifying high-impact features. This
analysis improved tool usability by 8% for end users.
● Developed XML-based ETL processes to integrate healthcare data sources, ensuring system
compatibility. This integration improved data accessibility for internal teams.
● Created Python scripts for automated data validation, ensuring 97% accuracy in operational reports.
These scripts minimized errors in critical deliverables.
● Documented data workflows, creating a reference guide for internal team use. This documentation
reduced onboarding time for new analysts by 10%.
● Utilized JavaScript to enhance web-based report interactivity for internal tools, improving user
engagement. These enhancements increased report adoption by 12%.
● Evaluated third-party data sources for healthcare analytics, expanding data coverage by 15%. This
expansion improved the accuracy of operational forecasts.
● Trained team members on Tableau usage, enhancing visualization capabilities for internal tools. This
training improved dashboard development efficiency by 15%.
● Monitored data pipelines using AWS CloudWatch, ensuring 98% uptime for internal systems. This
monitoring minimized disruptions in data delivery.
● Presented operational insights to project leads, driving strategic decisions for tool development. These
insights contributed to a 7% increase in operational efficiency.
● Developed reusable SQL templates for recurring operational reports, reducing development time by
15%. These templates standardized reporting across internal teams.
● Optimized data segmentation for healthcare analytics, improving targeting for operational strategies.
These efforts increased process efficiency by 10%.
● Integrated Google Cloud BigQuery for ad-hoc operational analysis, reducing query times by 12%. This
integration supported rapid decision-making for internal projects.
● Performed root cause analysis on data discrepancies, resolving 90% of issues within 24 hours. This
rapid resolution ensured timely delivery of operational reports.
● Created automated data quality checks using Python, ensuring high reliability in operational
deliverables. These checks reduced errors by 10% in internal reports.
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