Suhas Vaitla

Spearheaded migration of enterprise data from Azure SQL Database to Azure Synapse Analytics, optimizing reporting capabilities and minimizing analytics latency.

Coordinated efforts among data engineers, analysts, and BI developers in re-architecting pipelines and reporting structures aligned with modern practices.

Enhanced Power BI reporting infrastructure by integrating it with Synapse, resulting in improved dashboard performance and real-time executive insights.

Created an AI-driven automated query converter utilizing OpenAI technology, achieving over 60% reduction in manual effort during legacy query translation.

Led a team of five offshore developers in transitioning on-premises analytics solutions to Azure cloud components.

• Collaborated with cross-functional teams to implement data-driven solutions.

Implemented the Ohio project, transferring Ohio State insurance data to Azure Data Warehouse and then to third-party SMILE CDR.

Oversaw successful migration of Jiva-based insurance data to Azure Data Warehouse as part of Milliman project for third-party use.

Streamlined HEDIS project by transforming 21 lines of business data into actionable formats for Inovalon.

Engineered scalable cloud data solutions to facilitate transformed data loading into data warehouse.

Developed end-to-end templates for Azure Data Factory and production deployment release pipelines.

Architected solutions leveraging Azure DevOps, Azure Data Factory, Azure Data Lake, Azure Databricks, and Snowflake for stakeholder needs.

Utilized Azure Event Hub to capture real-time energy costs at bridges, with storage in ADLS for analytical purposes.

Developed statistical analyses to extract insights from extensive datasets employing machine learning and optimization tools.

Implemented Azure Pipelines and linked services for seamless data transfer to Azure cloud, utilizing ADLS for storage.

Delivered presentations to management featuring business recommendations supported by timely status reports.

Created ad hoc data validation reports with Python, automated scheduling via Apache Airflow.

Constructed process flows in Azure Data Factory and Azure Data Bricks for various ad-hoc requests.

Crafted interactive dashboards using data visualization tools like Power BI and Tableau for insightful reporting.

Utilized Python for extracting significant datasets from Oracle, Teradata, and SQL Server for retail pricing validation.

Engineered ML algorithms on Azure ML Platform, optimizing pricing logic based on independent variables.

• Optimized existing ETL processes using modern tools and techniques for better performance.
• Conducted root cause analysis on data discrepancies to identify underlying issues.

Collaborated with stakeholders to identify business process inefficiencies and propose solutions enhancing revenue.

Architected data migration strategy from on-premises systems to Azure Cloud using Azure ADF for reporting on Azure Data Bricks.

Executed migration of on-premises data to Azure SQL Data Warehouse and Azure Data Lake Store utilizing Azure Data Factory V2.

Developed streaming pipelines with Kafka and Azure Data Bricks for data transformation, integrating Power BI with Azure Event Hub.

Advised on cost efficiency strategies for infrastructure optimization and future usage.

Implemented machine learning algorithms in Azure Data Bricks for demand forecasting and pricing analytics.

Created visualization dashboards on Power BI to present analytical insights.

Designed a model predicting parking lot occupancy for product loading and unloading at hubs.