Raj Katta

AI & Product Analytics

• Architected embedded product analytics infrastructure enabling B2B customers to self-serve operational insights on their own product usage
• Defined and standardized accuracy measurement methodology for Claude/OpenAI-powered document processing models, aligning engineering, product, and customer success behind a unified accuracy narrative
• Designed low-confidence score detection analytics to surface model errors, improving customer trust and model iteration cycles
• Delivered customer-facing operational metrics dashboards covering real-time product usage and AI model performance

Architecture & Leadership

• Built entire data architecture from the ground up at an AI-native company leveraging Claude and OpenAI for insurance document processing
• Modernized data stack by consolidating all data operations into a unified platform featuring Snowflake, FiveTran, and Omni

Team Building & Governance

• Pioneered "Data University," a company-wide upskilling program driving a 3x increase in daily active users and a shift toward self-service analytics
• Mentored cross-functional team members in DBT, SQL, and Git best practices
• Established formal data governance via RFC processes and On-Call SLA runbooks

Environment: DBT(Data Build Tool), Snowflake, SQL, Python, Azure, Blob store, Eventhub, Airflow, Elementary, Openflow

• Product & Experimentation Analytics
  Designed and executed A/B test frameworks for new B2C product launches, delivering hit/miss analysis that directly informed go/no-go decisions
• Conducted time series analyses on customer behavior to identify usage trends, enabling data-driven product feature prioritization

Data Architecture & Infrastructure

• Optimized UTM validation pipelines and data refresh cadences, reducing processing time by 80% and generating significant infrastructure cost savings
• Developed standardized EDW architecture with automated data integration processes, improving reliability and cross-system data management
• Integrated a robust data quality framework into existing architecture, improving data integrity and reliability across teams

Cost & Efficiency

• Implemented data retention optimization and query execution scheduling strategies, driving a projected $80–90K in annual cost savings
• Designed scalable query architecture that significantly reduced processing times and improved resource utilization across multiple concurrent projects

Leadership & Team Building

• Scaled the data team from 2 contractors to a lean, high-impact 4-person team over 2 years, establishing hiring processes and driving team culture from the ground up
• Led end-to-end data initiatives as the primary analytics stakeholder, partnering with product and marketing teams to define KPIs and deliver insights

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