Jitendra DONEPUDI

• Developed and implemented enterprise-wide data architecture strategies, aligning with business goals to enhance data integration, governance, and analytics capabilities.
• Designed scalable and efficient data models for transactional and analytical systems, incorporating best practices for data warehousing and Azure-based architectures.
• Led data architecture projects, defining technical requirements, project scope, and resource allocation to achieve optimal solutions.
• Architected cloud-based data platforms on Azure, leveraging Azure Synapse, Azure Data Lake, Cosmos DB, and other Azure-native services to support advanced analytics and machine learning applications.
• Collaborated with stakeholders, including data scientists, analysts, and executive leadership, to translate business needs into robust data models and architectural solutions.
• Guided cross-functional teams in implementing data architecture patterns, such as Data Lake, Data Mesh, and Medallion Architecture, to support real-time data processing and analytics.
• Developed and optimized the Snowflake platform, achieving a 20-25% cost reduction through query tuning, clustering, and storage optimizations.
• Redesigned Snowflake data models to enhance efficiency, leveraging partition pruning, materialized views, and caching strategies to reduce query response times.
• Streamlined ETL/ELT processes in Snowflake using SnowSQL, Snowpipe, and Streams, improving data flow efficiency and maintaining data integrity.
• Established data governance frameworks, enforcing policies to ensure data security, quality, and compliance with industry standards.
• Collaborated with developers and platform architects to integrate data from multiple sources within Azure, ensuring consistency and accessibility for reporting and analytics.
• Mentored and guided data engineers in best practices for data architecture, Snowflake optimization, and ETL design, promoting continuous learning and innovation.
• Implemented monitoring and proactive alerts for Azure-based data architecture, minimizing downtime and improving system reliability by 30%.
• Evaluated emerging Azure technologies and services to drive continuous improvement and innovation in data architecture and platform management.

• Led cross-functional teams on high-visibility projects involving multiple departments within the organization.

• Developed database architectural strategies at modeling, design, and implementation stages to address business and industry requirements, ensuring scalability and performance.
• Created multi-site system architecture plans to reduce redundancy and improve resource utilization across the organization.
• Drafted conceptual and logical data models for high-level system planning, optimizing designs based on customer needs and budgets.
• Migrated legacy systems to modern architectures, reducing costs and enhancing efficiency by transitioning to newer technologies.
• Incorporated cloud architecture into new facility planning, reducing need for on-site equipment and technical support personnel.
• Improved database performance through query optimization, indexing strategies, and overall architecture design.
• Led design and development of Snowflake architecture for Cost of Care Executive Dashboard, enhancing data insights for business operations.
• Built semantic data models on Snowflake to support efficient querying and reporting, leveraging Materialized Views and Query Acceleration Service.
• Designed and implemented end-to-end ETL/ELT workflows for data integration using AWS Lambda, Glue, and SnowSQL, ensuring optimal data flow from source systems to Snowflake.
• Spearheaded Data Governance initiative, establishing a data dictionary and managing metadata using Snowflake's governance features.
• Led optimization efforts on Snowflake, improving cost efficiency by 20% through query performance tuning, compute optimization, and storage management with clustering keys and materialized views.
• Integrated Snowflake with AWS services (Redshift, S3) and managed data lifecycle and security using IAM and Data Encryption features.
• Developed ETL solutions for data ingestion pipelines, leveraging AWS Lambda Step Functions and SnowSQL to execute scripts and manage ETL processes.
• Ensured data security and lifecycle management using IAM and encryption features within Snowflake.
• Implemented analytic engineering techniques in Snowflake, including partition pruning and materialized views, to streamline data transformations and improve performance.
• Led integration of Snowflake with AWS services, ensuring efficient data flow and minimizing storage and compute costs through intelligent partitioning.
• Designed and implemented data warehouse solutions with ETL processes, business intelligence, and data visualization tools.
• Built semantic data models leveraging DBT for querying and reporting, aligning designs with client needs and architectural requirements.
• Reduced redundant processes by automating ETL tasks, increasing productivity and minimizing human error.

• • Architected data management solutions to support analytical and management applications, focusing on data transport, lineage, security, analysis, reporting, and lifecycle management.
  • Developed comprehensive ETL architecture for interface jobs, collaborating within SOA-integrated framework using Information Service Director and audit frameworks to ensure efficient business operations.
  • Designed and implemented Snowflake Schema data models to optimize reporting and analytical capabilities, enhancing query performance and data integration.
  • Designed and maintained robust data warehouse solutions tailored to evolving business requirements and strategic decision-making needs.
  • Led data migrations from on-premises systems to Snowflake, optimizing integration, improving performance, and supporting scalable cloud-based infrastructure.
  • Developed and implemented efficient ETL processes for data extraction, transformation, and loading, ensuring high data accuracy and consistency.
  • Collaborated with cross-functional teams to gather and analyze requirements, aligning data warehouse designs with business and stakeholder needs.
  • Performed comprehensive data modeling and schema design to create scalable data warehouse structures and optimize database performance.
  • Drove innovation and enhancements in data warehouse architecture, leading design and implementation projects that improved overall data operations.
  • Participated in all phases of project delivery, including PoC, business requirements analysis, data modeling, ETL design, SIT, UAT, production deployments, and warranty support.
  • Developed ETL frameworks leveraging DBT to streamline data ingestion and transformation, establishing consistent coding standards across data pipeline for improved efficiency.
  • Optimized query performance and data retrieval processes to enhance system efficiency and user experience within data warehouse.
  • Demonstrated expertise in ETL architecture by designing ETL code, performing unit testing, and conducting systems and regression testing to ensure robust data operations.
  • Solved complex technical challenges by creating ETL framework for ODS using Goldengate, supporting ad-hoc reporting and integrating Teradata14 for efficient data transformation.
  • Implemented AWS cloud-based solutions (Amazon S3, Glue, IAM, Redshift, Lambda, RDS) to create scalable and efficient data management frameworks.
  • Provided technical support and troubleshooting expertise to resolve data warehouse issues, ensuring uninterrupted business operations.
  • Maintained data quality and integrity through continuous monitoring, validation, and quality checks in data warehouse.
  • Developed reusable DataStage and dynamic schema-generated jobs, minimizing object redundancy while building efficient CDC components and surrogate key sequences for historical data tracking.

• Collaborated with cross-functional teams to gather requirements, define project scope, and deliver customized data solutions.
• Maintained communication channels with clients, providing regular updates on project status and addressing concerns promptly to ensure satisfaction.
• Established efficient workflows by automating routine tasks using scripting languages and scheduling tools.
• Managed end-to-end development lifecycle for multiple data warehousing projects, ensuring timely delivery and meeting client expectations.
• Oversaw Mid-Level Platform Application Design and drafted System Design Document, initiating ETL process documentation, mapping, and conducting comprehensive ETL code reviews to enhance performance and quality.
• Coordinated with CM/Testing teams to ensure release notes synchronization with other departments while conducting ETL architecture reviews for alignment with standards.
• Applied project management skills to gather business requirements, estimate testing efforts, and forecast resources while using parallelism concepts to distribute workloads efficiently among processors.
• Achieved project goals by writing UNIX shell scripts to automate DataStage processes and implementing CDC components and surrogate key sequences for efficient history loading jobs.
• Implemented database performance tuning and optimization strategies to enhance speed and efficiency of the data warehouse.
• Designed data models and schemas for scalable and reliable data warehousing solutions, aligning with business requirements and architectural standards.
• Managed and optimized ETL processes for handling large volumes of data, ensuring smooth data flow and effective transformation to meet reporting and analytical needs.
• Led and mentored a team of developers in building and delivering robust data warehouse solutions, fostering collaboration and adherence to best practices.
• Developed and maintained data pipelines to extract, transform, and load data from various sources, ensuring consistency, quality, and scalability.
• Coordinated cross-functional efforts to align with organizational goals, managing system integration, and overseeing the lifecycle of data warehouse implementations.