Goutham Thota

Designed and maintained real-time data pipelines processing over 5 million daily transactions using Apache Kafka, AWS Kinesis, and Spark Structured Streaming, reducing fraud detection time by 40% and increasing customer personalization accuracy by 30%. Developed ELT workflows using dbt, Snowflake, and Python, transforming raw financial and customer data into structured, analytics-ready datasets, improving reporting speed and enabling real-time insights across business units. Built and orchestrated cloud-native ETL pipelines with AWS Glue and Apache Airflow, automating data ingestion from APIs, third-party vendors, and internal banking systems into Redshift and Snowflake for advanced analytics. Implemented robust data quality and validation frameworks using Python, AWS CloudWatch, and Great Expectations, proactively identifying schema changes, data drift, and anomalies in production pipelines. Provisioned 100+ AWS resources using Terraform to deploy scalable, compliant data platforms, r

Engineered scalable data pipelines in Apache Spark, Azure Data Factory, and Azure Databricks to process over 10 TB of EHR, claims, and encounter data weekly, improving care coordination efficiency by 35% and accelerating model training time for predictive analytics by 40%. Designed and optimized dimensional healthcare data models in Snowflake and Azure Synapse Analytics, supporting high-performance querying and BI reporting across clinical, pharmacy, and utilization datasets. Automated ETL workflows to ingest HL7/FHIR data from EMR systems and third-party providers using Talend and Python, ensuring secure, accurate, and timely integration of critical patient data into centralized healthcare data lakes on Azure Data Lake Storage (ADLS). Developed HIPAA-compliant batch and streaming pipelines in Apache Airflow and Azure Event Hubs to process 1M+ patient monitoring and claims records daily, ensuring 99.9% SLA adherence and real-time availability of critical health data for clinicians and