Data Engineer

Led the integration of healthcare data standards like FHIR, HL7, and EDI into ETL pipelines to ensure seamless interoperability across EHR and EMR systems. Automated data ingestion from clinical, claims, and lab systems using Apache NiFi, reducing manual handling by 60%. Developed modular Python-based ETL scripts for data extraction, transformation, and validation, boosting pipeline throughput by 40%. Designed scalable healthcare data warehouses and star-schema models using ERwin Data Modeler, improving analytical query performance by 30%. Created subject-specific data marts to support self-service analytics and enhance population health insights, resulting in a 20% gain in performance. Optimized complex SQL queries on large EMR and claims datasets, increasing claims adjudication speed by 35%. Ensured HIPAA compliance by configuring Azure Key Vault and enforcing RBAC across platforms. Built real-time streaming pipelines using Apache Kafka and Spark Streaming to deliver critical care alerts, reducing latency by 35%. Developed KPI-driven operational dashboards with Apache Flink to accelerate decision-making across hospital systems by 20%. Crafted executive-level Tableau dashboards to visualize patient readmission trends, resource utilization, and claims metrics. Established a comprehensive data governance framework using Apache Atlas and Collibra, improving audit readiness by 50%. Deployed proactive system monitoring and alerting solutions with Prometheus and Grafana, cutting pipeline downtime and incident response time by 25%.

Established a robust data governance framework using Apache Atlas for automated lineage tracking and metadata management, ensuring RBI audit readiness. Implemented advanced data security with AWS KMS for column-level encryption and Apache Ranger for fine-grained access control, aligning with RBI and GDPR regulations. Designed and optimized scalable ETL pipelines with Apache Spark and Airflow, reducing data latency by 40% for high-volume banking transactions while integrating seamlessly with core banking platforms. Tuned Hadoop clusters for enhanced performance, improving system throughput by 30% through targeted resource allocation and configuration. Built real-time fraud detection pipelines using Apache Kafka and Spark Streaming to analyze millions of daily transactions, fortifying financial security in accordance with RBI fraud prevention mandates. Developed a Python-Spark-based data profiling tool to detect and resolve data quality issues, significantly boosting accuracy in regulatory reporting. Architected a self-service analytics platform on Databricks, enabling predictive modeling for credit risk and customer segmentation. Created interactive Power BI dashboards delivering real-time insights into customer behavior, loan performance, and banking product metrics to support data-driven decision-making. Automated CI/CD pipelines with Jenkins and Docker, cutting release cycles by 70% and accelerating delivery of data solutions. Deployed a 24/7 monitoring system using the ELK Stack, ensuring real-time observability, proactive issue resolution, and operational resilience across critical banking data infrastructure.