Naveen Kothi

1. Led design and optimization of large-scale ETL/ELT pipelines powering fraud, credit, AML/KYC, and risk analytics, improving processing efficiency by 45%. 2. Architected real-time ingestion and decisioning pipelines integrating Kafka, APIs, and secure file feeds with 99.9% uptime for regulatory workloads. 3. Increased SQL and Snowflake performance by 60% through partitioning strategies, clustering, and compute optimization at scale. 4.Designed PCI-DSS and SOX-compliant data architectures supporting high-volume payments, treasury, and reconciliation systems. 5. Delivered production-grade feature stores enabling fraud detection, anomaly detection, and credit scoring models. 6.Modernized legacy SSIS/Informatica workloads to Databricks and Snowflake, reducing batch windows by 50% and improving reliability. 7. Developed reconciliation frameworks reducing data mismatches between OLTP and analytics platforms by 40%. 8. Strengthened enterprise governance through encryption, tokenization, lineage tracking, and RBAC-based access controls.

1.Designed and optimized ETL/ELT pipelines across AWS and Azure using SQL, Python, PySpark, AWS Glue, ADF, Databricks, BigQuery and Snowflake to process claims, eligibility, encounters, pharmacy and provider datasets, reducing latency by 40%. 2.Developed healthcare data models (Member, Claims, Provider, Encounter, Utilization) using star schema, dimensional modeling and data marts, integrating structured, semi-structured (FHIR, HL7, X12 EDI, JSON, XML) and unstructured clinical data to improve accessibility for actuarial, clinical and analytics teams. 3.Engineered scalable, PHI-compliant pipelines and analytical solutions using MongoDB, Hadoop, Hive and cloud data lakes (S3/ADLS), enabling secure ingestion and processing of millions of clinical and claims records. 4.Built real-time ingestion pipelines using REST APIs, Kafka, Lambda and Azure Functions to automate near real-time claims adjudication, eligibility validation and provider updates, reducing manual workflows from days to hours. 5.Delivered BI dashboards (Power BI, Tableau, Looker) for HEDIS, Stars Rating, risk adjustment, utilization management and provider performance, improving clinical operations and enabling 35% faster insights. 6.Built serverless data workflows using Java, Python, AWS Lambda, Glue ETL and CI/CD (GitHub Actions, Jenkins, Azure DevOps), accelerating release cycles by 30% and improving pipeline reliability and cloud efficiency. 7.Mentored teams on healthcare data engineering best practices, created internal knowledge guides and partnered with data scientists to deliver standardized datasets for predictive healthcare models such as readmission risk, chronic disease prediction and utilization forecasting.

Completed a comprehensive Master’s program in Computer Science , Focused on software engineering, data management and cloud computing. Gained hands-on experience designing and developing scalable data-driven applications using Python, SQL and modern database systems. Worked on projects involving ETL workflows, data modeling and analytics—building foundations for data engineering pipelines and cloud-native solutions across AWS and Azure. Developed a strong understanding of algorithms, data structures, distributed systems and big data frameworks, bridging computer science fundamentals with real-world data architecture and analytics applications.