Durga Prasad Katasani

Durga Prasad Katasani is an enterprise data architect whose eighteen-year career tracks the evolution of modern data engineering itself—from COBOL and JCL batch processing on mainframes to cloud-native data ecosystems and AI-enabled observability at Salesforce. What distinguishes Katasani’s trajectory is not just longevity, but breadth with continuity: he understands legacy infrastructure at the mechanical level, and he designs contemporary cloud platforms with the discipline that comes from operating where reliability, governance, and audit constraints are non-negotiable.

His technical mastery spans the full enterprise data stack. In Snowflake, he brings architect-level depth across dynamic data masking, continuous ingestion patterns, time travel/cloning, materialized views, Snowpipe, streams/tasks, micro-partitions, clustering, and CDC design—capabilities that directly shape security posture, performance, and cost efficiency in production warehouses. He complements this with Databricks Lakehouse expertise—Delta Lake, Delta Live Tables, Unity Catalog, and orchestration—enabling unified analytics architectures that support governed self-service without losing traceability. Across cloud providers, he has delivered architectures on AWS (S3, EC2, RDS, Redshift, Lambda, Glue, Athena) as well as Azure and GCP, and he has led complex integration programs using ETL/ELT platforms including DataStage, Informatica, Matillion, Fivetran, Azure Data Factory, Dell Boomi, Talend, and Qlik.

At Salesforce, Katasani operates at the frontier where data architecture meets AI and operational excellence. As a Senior Data and AI Architect, his work on the AI Factory initiative focuses on centralizing AI/ML enablement across core enterprise workflows—DevOps, ITSM, Testing, and Operations—so that intelligence is delivered as a platform capability rather than a series of one-off projects. He has also designed RAG-based discovery solutions that make Enterprise Architecture Review Board materials searchable and usable through structured tagging, smart chunking, embeddings, and vector indexing—an applied approach that turns institutional knowledge into operationally accessible decision support. In parallel, as Senior Data and Observability Architect, he conducted gap assessments and architected observability across Snowflake, Tableau Cloud, IDMC, and Amazon Managed Airflow—delivering monitoring for health, performance, availability, cost, and usage with predictive and reactive alerting integrated into ServiceCloud, Slack, and PagerDuty. The result is not only improved awareness, but faster root-cause isolation through automated diagnostics and AI-guided recommendations.

His prior roles show repeatable impact across industries. At TravelCenters of America, as Enterprise Architect and Snowflake SME, he defined enterprise-wide data architecture and led migrations from Oracle and SAP BW/HANA into a multi-layer Snowflake environment with Azure Data Factory ELT—cutting key report retrieval times by 80% and automating 200+ manual Excel reports into Power BI, reclaiming 100+ manual hours per week. At Tracfone Wireless, he drove Snowflake migrations and performance tuning using Matillion, reducing ETL processing time by 60% and improving query responsiveness for business users. At Belk, he led integration architecture across major retail platforms and designed Teradata-to-Snowflake migration on Azure with near real-time processing for sales, transaction, and inventory data. At Lime and Worley, he architected financial and people-data migrations into Snowflake using Oracle BICC and tools like Dell Boomi and Informatica Cloud—ensuring accuracy, completeness, and compliance. Earlier at Bank of America, he delivered MDM and reference data integration while leading production support improvements that reduced incident rates and resolution times by 50%—evidence of operational maturity developed long before “platform reliability” became a mainstream architectural mantra.

Beyond delivery, Katasani contributes to institutional capability building. Through Snowflake and Databricks Centers of Excellence work (Capgemini), he establishes best practices, governance standards, documentation, and training programs, and supports presales architecture via cost modeling and ROI analysis—helping organizations adopt modern data platforms responsibly and effectively. Backed by advanced certifications across Snowflake, Databricks, AWS, and Azure, Katasani’s profile reflects a sustained pattern: engineering governed, scalable data systems that reduce cost, accelerate insight, and make operations more intelligent.