Ravi Teja

Results-driven Data Analyst with extensive experience in healthcare and financial services, specializing in data analysis using SQL, Python, and Excel. Proficient in creating interactive dashboards with Power BI and Tableau to support business intelligence. Successfully led healthcare analytics projects, improving operational efficiency and compliance. Eager to leverage expertise in data solutions to drive strategic decision-making in a dynamic environment.

Data Analyst - McKesson

(2023-08)

• Built a scalable data pipeline using Talend, Apache Spark, and Azure Data Factory to integrate high-volume pharmaceutical supply chain data, enhancing data processing efficiency and accuracy
• Utilized SQL, Python (Pandas, NumPy, SciPy), and R to perform advanced exploratory data analysis on medication distribution trends, resulting in a 15% improvement in operational efficiency across 50+ distribution centers
• Created interactive Power BI dashboards and Tableau visualizations that provided real-time insights into drug inventory, delivery SLAs, and DEA compliance for executive leadership and logistics teams
• Worked with Azure Synapse Analytics and Azure Data Lake to store and analyze petabyte-scale healthcare datasets, reducing report generation time from 2 hours to 10 minutes using optimized query structures in Snowflake and Hive
• Partnered with Compliance & Risk teams to build predictive models in Scikit-learn and R, helping detect anomalies in controlled substance shipments and automate reporting to regulatory authorities via Airflow and Jenkins pipelines
• Designed and automated ETL workflows using SQL, Python, and Talend to ingest data from PostgreSQL, MySQL, Oracle, and Snowflake, ensuring high data availability and governance for analytics consumption
• Built and deployed ML models using Scikit-learn and Azure Machine Learning Studio, with MLOps orchestration through GitHub Actions, Jenkins, and Docker on Azure Kubernetes Service (AKS)
• Led the implementation of analytics solutions on the Azure platform, including the use of Azure Data Lake, Azure SQL Database, and Azure DevOps for CI/CD pipeline management and infrastructure provisioning with Terraform
• Utilized Apache Kafka and Airflow for real-time data streaming and job orchestration, enabling on-demand processing of millions of patient and logistics records with HDFS and Hive integration, improving data processing speed
• Conducted data profiling, validation, and cleansing using Python and Excel, ensuring high data integrity for analytics and reporting purposes while maintaining audit compliance using Git and Bitbucket version control

Data Analyst - Macropace Technologies

(2021-02 - 2022-05)

• Assisted in the collection, cleaning, and analysis of over 150,000 customer interaction records to identify key purchase trends, early churn indicators, and user behavior patterns, contributing to actionable business insights
• Designed clear, actionable dashboards using Power BI to visualize KPIs such as session duration and cart abandonment, enabling performance tracking for retail clients
• Wrote complex SQL queries to extract customer and transaction data from PostgreSQL databases, generating monthly trend reports to track performance and identify opportunities for process optimization in customer engagement
• Participated in cross-functional meetings with product and marketing teams, presenting data-driven insights that led to improvements in campaign targeting, driving a 12% increase in the effectiveness of marketing campaigns
• Performed cohort and funnel analysis to identify user drop-offs and optimize user retention strategies, recommending UI/UX improvements based on data findings that aimed to reduce churn and improve the customer experience
• Supported the efficient operation of data pipelines using AWS Glue & S3, ensuring smooth transformation of structured data (CSV/JSON) for analytics processes, which contributed to improved data workflow automation
• Built visually engaging charts in Amazon QuickSight to track customer engagement metrics, empowering stakeholders with the data needed to make informed decisions about marketing strategies
• Queried large datasets stored in Amazon S3 using AWS Athena, conducting ad-hoc analysis and generating detailed reports in Excel and Python (pandas) to support data-driven decision-making across the organization
• Ensured the smooth operation of scheduled ETL jobs via AWS CloudWatch, troubleshot issues and reported them to senior data engineers for resolution, ensuring minimal data pipeline disruptions
• Participated in data validation and consistency checks for customer analytics data stored in Amazon Redshift, ensuring data quality, integrity, and reliability to maintain accurate analytics for business decision-making

Masters - Computer Science - University Of Missouri Kansas City (2022-08 - 2024-05)

Bachelor - Computer Science - SRM University (2018-07 - 2022-07)