Raj Katta

Key Achievements:

• Optimized Data Processing Architecture: Enhanced UTM validations and improved data refresh frequency, resulting in 80% reduction in processing time and significant cost savings.
• Streamlined EDW Implementation: Developed standardized architecture and automated processes to facilitate efficient data integration and management across systems.
• DMS Decommissioning Support: Established foundational architecture for transitioning to a new data environment, ensuring smooth migration and reduced operational risks.
• Data Quality Improvement: Integrated a robust data quality system into the existing architecture, enhancing data integrity and reliability across teams.
• Cost Reduction Initiatives: Implemented architectural strategies that resulted in a projected annual savings of $80-90K through optimized data retention and query execution schedules.
• Enhanced Scalability and Efficiency: Designed an architecture that significantly reduces query processing times and increases scalability, enabling effective resource utilization across multiple projects.

Environment: DBT(Data Build Tool), Snowflake, SQL, Python, AWS, Redshift, DMS, Airflow, Elementary

Key Achievements:

• Automated Data Transformation Architecture: Established a robust pipeline using Airflow to automate over 20 manual processes, streamlining data transformation and enhancing workflow efficiency.
• Modular Design for Cancer Data: Developed new modules to integrate various cancer types and test results from diverse health systems, improving data accessibility for analysis.
• Standardization with OMOP Model: Transformed cancer data into the OMOP common data model, ensuring compatibility and acceptance across the healthcare industry for better data utilization.
• SQL Query Optimization: Increased efficiency by optimizing SQL queries, significantly reducing the time required to execute data processing modules and improving overall performance.
• Enhanced Data Analysis Capabilities: Provided structured and standardized data to support doctors and researchers in analyzing cancer metrics, facilitating more informed decision-making.
• Improved Collaboration Across Systems: Designed an architecture that enhances collaboration and data sharing between healthcare systems, promoting comprehensive cancer research and insights.

Environment: SQL, Python, Airflow, Redshift, AWS services

Key Achievements:

• Built Comprehensive Data Architecture: Developed data architecture from ground up by understanding diverse business use cases across organization, ensuring alignment with company objectives.
• Automated Data Transformation: Utilized Apache Airflow to schedule daily jobs, streamlining data transformation processes and enhancing operational efficiency.
• Implemented Data Vault Modeling: Created data vault schemas that enable easy analysis by teams, promoting data accessibility and collaboration across ClickUp.
• Integrated Diverse Data Sources: Successfully migrated large volumes of data from Redshift and RDS to Snowflake, consolidating data from eight sources into a common data lake with optimized schemas.

Environment: SQL, Python, Snowflake, Redshift, Postgres, Fivetran, RDS, AWS services

Key Achievements:

• Developed Visual Analytics Dashboards: Created Looker and QuickSight dashboards to effectively visualize business use cases, enhancing client engagement and insights.
• Delivered Tailored Data Solutions: Designed and implemented solutions to meet client data requests using AWS resources like Lambda and Databricks, with DataDog for monitoring.
• Engineered Snowflake Data Architecture: Built a robust data architecture in Snowflake to support internal business intelligence analytics across Freewheel.
• Managed Client Relationships: Successfully managed two clients over eight months, providing customized visualizations and data solutions tailored to their specific requirements.
• Environment : Databricks, SQL, Looker, Quick-sight, Lambda, ECS, Spark, DataDog, Snow flake, Excel.

Key Achievements:

• Led Large-Scale Data Projects: Managed comprehensive data projects encompassing data modeling, ETL development, and data warehousing to support organizational goals.
• Designed Cloud-Focused Data Architecture: Developed an efficient and scalable data architecture that is GDPR compliant, facilitating targeted customer analysis for analysts.
• Implemented Robust Security Measures: Planned and executed security protocols to safeguard sensitive data, ensuring compliance with industry regulations.
• Established Data Accuracy Verification: Developed processes to verify data accuracy, enhancing the reliability and integrity of analytics across the organization.
• Environment : GO, bash, SQL, python, Airflow, AWS(EC2, Athena, S3, Quicksight), Azakaban.

• Developed various Machine Learning models to predict riskiest node from various metrics identified
• Research to predict and formulate resilience of various nodes present in supply chain based on various factors.
• Designed and developed various risk metrics and test efficiency of risk metrics
• Environment: C#, Python, Neural Network model, Confidence Factor model, Decision tree model