Mounika K

Designed and delivered real-time and batch data pipelines for banking, fraud detection, and risk analytics using Apache Kafka, Spark, and Azure cloud services. Built high-throughput event-driven architectures and optimized large-scale data processing on Azure Databricks, significantly reducing latency and improving performance. Orchestrated data ingestion and integration using Azure Data Factory, consolidating on-prem and cloud data into secure Azure Data Lake architectures. Migrated legacy ETL workloads to PySpark and distributed processing frameworks to improve scalability and reliability. Implemented data modeling, governance, security, and validation to meet regulatory requirements, while enabling enterprise analytics and executive reporting through Azure Synapse and Power BI.

Designed and delivered large-scale healthcare data platforms supporting claims processing, population health analytics, and risk assessment using AWS and big data technologies. Built real-time and batch pipelines with Apache Kafka, Spark, and AWS Glue to process high-volume medical and claims data with improved performance and reliability. Developed and optimized distributed ETL workflows using Spark, Scala, Teradata SQL, and HDFS, significantly reducing processing time and improving data accuracy. Implemented secure data lake and warehouse solutions on Amazon S3 and Redshift with strong governance, MDM, and HIPAA compliance controls. Enabled advanced analytics, executive reporting, and machine learning use cases through Power BI, QuickSight, and Amazon SageMaker.

Built and maintained scalable HR and financial data platforms using Python, SQL, and cloud-native services to support real-time and batch analytics. Developed ingestion, cleaning, and transformation pipelines using ETL frameworks, streaming ingestion, and event-driven architectures, improving data accuracy and reducing latency. Leveraged distributed processing with Apache Spark and cloud data warehouses to enable enterprise reporting and analytics. Implemented BI dashboards and ad-hoc analytics to provide workforce and financial insights. Automated infrastructure, workflows, and deployments using Airflow, Terraform, Docker, Kubernetes, and CI/CD pipelines while enforcing strong data security and access controls.

Designed and delivered a cloud-based insurance data integration platform on AWS to unify policy, claims, and customer data, improving integration efficiency by 40%. Built and orchestrated scalable batch pipelines using AWS Glue, Amazon EMR, and Apache Airflow to streamline ingestion and transformation of insurance datasets. Led large-scale migration of legacy policyholder data to modern cloud platforms, ensuring data accuracy, continuity of coverage, and minimal downtime. Implemented PySpark-based transformations and optimized Amazon Redshift queries to significantly reduce processing time and improve analytics performance. Established secure, compliant data storage and reporting solutions using Amazon S3, RDS, Tableau, and SageMaker to enable underwriting, risk assessment, fraud detection, and executive decision-making.

Designed and delivered scalable ETL solutions using AWS, Informatica, and Talend to integrate data from multiple client systems, improving data availability by 40%. Built secure and efficient data ingestion and transformation pipelines using Amazon S3, AWS Glue, and Informatica PowerCenter to support large-scale client analytics. Optimized Talend jobs and complex transformation logic to reduce processing time by 30% and improve data accuracy by 25%. Automated ETL scheduling, monitoring, and alerting using AWS Lambda and CloudWatch, significantly reducing manual intervention and downtime. Enabled client reporting and decision-making through optimized Redshift loads, Tableau dashboards, and strong data security and compliance controls.