Data Analyst Supply Chain

Akshatha R Rajanala

Data Analyst

Email: *****************@*****.***

Mobile: +1-480-***-****

Linkedin URL: LinkedIn

PROFESSIONAL SUMMARY

Data Analyst with 5+ years of experience delivering data-driven solutions across supply chain, healthcare, and academic sectors. Proven ability to extract insights from complex datasets using SQL, Python, Tableau, and Power BI. Strong background in statistical modeling, data storytelling, and cloud analytics (AWS, Azure, Snowflake). Passionate about leveraging data to drive performance, optimize logistics, and enable strategic decision-making. Experienced in data governance, ETL automation, and cross-functional collaboration to deliver impactful business intelligence solutions.

EDUCATION

Masters in Applied Econometrics with Data Analysis Wichita State University – May 2024

Coursework: Data Analytics, Data Visualization, SQL, Business Analytics, and Econometrics.

Certifications: IBM Databases and SQL for Data Science, Google Data Analytics Professional Certificate, PWC Power BI Simulation.

TECHNICAL SKILLS

Data Analytics & ETL: SQL, Excel, Python, Alteryx, SPSS, Stata, SAS

Data Visualization: Tableau, Power BI, Matplotlib, Seaborn

Cloud & Data Engineering: Snowflake, AWS (Glue, Redshift, S3), Azure Data Factory

Machine Learning & Statistical Modeling: Regression Analysis, Predictive Analytics, Time Series Analysis

Spreadsheet Tools: Microsoft Excel, Google Spreadsheet

Specializations: Econometrics, Data Architecture, Data Governance, Alteryx workflows, Data Integration (ETL, Data Warehousing, SAS Data Integration Studio), Predictive Analytics, Healthcare data analytics.

PROFESSIONAL EXPERIENCE

WMS Data Analyst,

William Sonoma. Inc, MS Aug 2024 – Present

Spearheaded the analysis of Warehouse Management System (WMS) workflow data to streamline supply chain operations, improving efficiency in picking, packing, labeling, and shipping, leading to a 15% improvement in operational timelines and cost savings.

Applied statistical modeling (regression, hypothesis testing, time series analysis) to forecast demand fluctuations, improving operational efficiency.

Optimized warehouse operations using SQL, Python (Pandas, NumPy, Dask, Polars), reducing order processing errors by 18% and improving inventory accuracy by 20%.

Developed and implemented a comprehensive statistical analysis framework using SAS and Python, which improved operational efficiency by 13% and reduced costs by 15%.

Developed predictive models in Python to identify supply chain bottlenecks, resulting in a 15% reduction in shipping delays and stock-outs.

Built and optimized ETL pipelines using tools like SAS Data Integration Studio, ensuring seamless data integration and reducing manual processing efforts by 40%.

Established and enforced data governance protocols using AWS and Azure-based frameworks, ensuring compliance and data quality across cloud-based analytics environments.

Designed data pipelines in AWS Glue & Snowflake, integrating WMS, sales, and logistics data, improving reporting speed by 25%.

Developed scalable data models and data marts to support business intelligence initiatives, enabling faster insights into operational KPIs and reducing analysis turnaround time by 30%.

Automated data quality checks and anomaly detection scripts using Python and SQL, ensuring real-time monitoring and improving data reliability across WMS operations.

Supported ad-hoc data requests and resolved production issues, ensuring timely and accurate reporting for stakeholders.

Built automated Power BI and Tableau dashboards to track supply chain KPIs, order fulfillment, and delivery trends, reducing reporting time by 40%.

Implemented predictive analytics using historical WMS data, and machine learning techniques in Python, integrating data from multiple sources through ETL pipelines. This enhanced forecasting of supply chain bottlenecks and optimized inventory management, leading to a 20% reduction in delays and stock-outs during peak seasons.

Collaborated with IT teams to integrate WMS data into cloud warehouses (AWS, Azure, Snowflake), improving scalability and analytics performance.

Partnered with logistics and fulfillment teams to optimize shipping and delivery operations through advanced analytics

Graduate Assistant – Department of Economics

W. Frank Barton School of Business Mar 2023 – Jun 2024

Performed econometric analysis on large datasets using Excel, Stata, and R, generating research insights for academic publications and conferences.

Designed Tableau dashboards to visualize economic indicators and departmental trends, aiding curriculum development.

Supported professors by grading over 150 assignments per semester, ensuring accuracy and timely feedback for students.

Leveraged SPSS for survey data analysis, regression modeling, and hypothesis testing, while using Stata for econometric research, time series analysis, and panel data modeling.

Data Analyst Student Intern

Centre for Strategic Health Innovation Sep 2022 – Jan 2023

•Collaborated with the IT team to analyze healthcare insurance data in Medicaid and Medicare for Alabama, using data analytics techniques. These insights informed software development decisions, leading to a 15% improvement in system efficiency.

•Utilized Tableau to create impactful data visualizations that effectively communicated complex healthcare data insights. These visualizations played a key role in team meetings, presentations, and strategic decision-making processes for healthcare innovation initiatives. Analyzed claims data in AWS to identify high-cost utilization patterns, leading to a 10% reduction in program costs.

•Automated data processing pipelines using AWS and SQL, reducing manual data handling time by 30%.

•Acquired and applied new skills in data analytics, utilizing Tableau to create visualizations and AWS for database management, enhancing team capabilities. Contributed to the preparation and presentation of data reports, aiding in strategic decision-making processes for healthcare innovations.

•Collaborated with IT teams to implement data pipelines and ensure data quality, supporting the organization’s mission.

Data Analyst

Providence Healthcare Company Jun 2019 – Aug 2022

Extensive experience in analyzing claims, provider, and patient data to drive operational improvements and cost savings. Analyzed large-scale claims data to identify trends, optimize operational efficiency, and improve cost management.

Developed and maintained ETL pipelines using AWS Glue and EMR, integrating claims, provider, and patient data into a centralized data warehouse, improving data retrieval speed and reporting efficiency by 15%.

Developed and implemented a comprehensive healthcare analytics dashboard using Tableau, which improved patient care metrics by 10% and reduced operational costs by 15%.

Analyzed healthcare insurance data using SQL and Excel to identify high-cost utilization patterns, leading to a 10% reduction in program costs.

Designed and automated ETL pipelines for healthcare data integration, ensuring seamless data migration across Azure SQL databases. Implemented Azure-based data governance frameworks, increasing data accuracy by 12%.

Utilized AWS services (such as S3 and Redshift) for efficient data storage and querying of large-scale healthcare data, improving data retrieval speed and reporting efficiency by 20%.

Leveraged Azure Data Factory to integrate data from multiple sources (such as claims, provider, and patient data) into a centralized data warehouse, enhancing the accuracy and completeness of analytics and reporting.

Collaborated with healthcare providers to implement value-based contracting strategies, resulting in a 15% improvement in patient outcomes and a 12% reduction in costs.

Developed Power BI dashboards that reduced reporting time by 40% and enabled real-time decision-making, improving executive insights by 12%

Analyzed healthcare claims data using SQL and Python, uncovering cost-saving trends that improved operational efficiency by 15%. Implemented process enhancements reducing administrative delays.

Conducted predictive modeling using Python (scikit-learn) to forecast patient readmission risks, resulting in targeted interventions that reduced readmission rates by 18%.

Partnered with cross-functional teams to design and implement HIPAA-compliant data pipelines, ensuring secure and scalable handling of sensitive healthcare data, leading to a 20% increase in regulatory audit readiness.

ORGANIZATIONS AND LEADERSHIP

Dean's Graduate Student Advisory Board: Collaborated on strategic initiatives to enhance student programs and academic offerings at the Barton School of Business.

Economists Anonymous: Networked with industry leaders and participated in workshops to strengthen professional insights in data analytics.

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