Praveen Adapala

• Designed and implemented robust data pipelines in Azure Data Factory, ensuring data security and automating data workflows for efficient data management and processing across multiple sources. • Contributed to modernization of data platform toward lakehouse architecture, supporting scalable ingestion, transformation, and reporting workflows • Utilized strong SQL knowledge for complex querying and optimization within Azure SQL, enhancing database design and ensuring efficient data retrieval for critical business intelligence reports. • Managed data pipelines using Azure DevOps, focusing on security and automation to ensure data integrity and compliance with industry standards, while adhering to best practices. • Leveraged Python for data engineering tasks, integrating with Azure services to streamline data processing and analysis, while utilizing libraries like Pandas and PySpark. • Implemented best practices for cloud-based database management, including backup, disaster recovery, and monitoring, ensuring high availability and data protection across all systems. • Applied schema design principles and performance optimization techniques to both relational and NoSQL databases, ensuring efficient data storage and retrieval for various applications. • Demonstrated strong troubleshooting skills for performance issues in both data pipelines and databases, quickly identifying and resolving problems to maintain system stability and reliability.

• Designed, implemented, and managed time-series data solutions using Amazon Timestream, defining retention policies and optimizing querying for efficient data analysis and reporting purposes. • Utilized strong SQL knowledge for complex querying and optimization within Redshift, enhancing database design and ensuring efficient data retrieval for critical business intelligence reports. • Implemented secure data access patterns using IAM roles, secrets management, and encryption mechanisms, ensuring compliance and controlled access to sensitive datasets • Developed lightweight Python-based data access services and APIs to expose processed datasets for downstream applications • Collaborated on cloud-native data platform evolution toward Databricks-based lakehouse solutions, supporting scalable analytics and unified data processing • Managed data pipelines using AWS services, focusing on security and automation to ensure data integrity and compliance with industry standards, while adhering to best practices. • Leveraged Python for data engineering tasks, integrating with AWS services to streamline data processing and analysis, while utilizing libraries like Pandas and PySpark. • Built and optimized data pipelines on lakehouse architecture, integrating structured and semi-structured data using distributed processing frameworks

• Developed SQL queries for data extraction and analysis from Azure SQL, supporting data-driven decision-making and improving business insights across various departments and operational areas. • Utilized Python for data analysis tasks, leveraging libraries such as Pandas to process and analyze large datasets, supporting business intelligence and reporting requirements effectively. • Implemented data pipelines using Azure Data Factory, focusing on automation and ensuring data integrity for reliable reporting and analytics across multiple business units and stakeholders. • Applied schema design principles to create efficient data models in SQL Server, optimizing data storage and retrieval for improved reporting performance and analytical capabilities. • Contributed to database maintenance tasks, including performance tuning and optimization, ensuring efficient data processing and supporting timely delivery of critical business information. • Demonstrated troubleshooting skills by identifying and resolving data quality issues, ensuring data accuracy and reliability for informed decision-making across the organization. • Experienced in defining retention policies for data stored in SQL Server, ensuring compliance with data governance requirements and optimizing storage costs effectively and efficiently.