Hema Sundari Gelli

Built healthcare data ingestion pipelines using Apache Kafka, AWS Glue, Python, and Amazon S3 to process pharmacy transactions, supplier shipments, and hospital inventory data for real-time analytics reporting., Developed scalable ETL workflows using PySpark, Databricks, Delta Lake, and dbt to clean pharmaceutical datasets, standardize product records, and support AI-ready healthcare business data models., Worked on Gen AI integration using LangChain, Azure OpenAI, Pinecone, and RAG architecture to generate supply shortage insights, shipment delay summaries, and AI-powered healthcare operational responses., Created Snowflake analytical datasets and AI-powered SQL query workflows using FastAPI, LangChain SQL agents, and OpenAI models, helping business teams access pharmaceutical reports through natural language queries., Managed workflow orchestration using Apache Airflow, Docker, Kubernetes, and Terraform to automate healthcare ETL jobs, AI document indexing, model refresh processes, and secure cloud infrastructure deployment., Improved healthcare data quality and governance using Great Expectations, OpenMetadata, AWS IAM, and HIPAA-compliant security controls, reducing reporting inconsistencies and supporting reliable Gen AI healthcare analytics workflows.

Built healthcare data ingestion pipelines using Apache Kafka, AWS Glue, Python, and Amazon S3 to process pharmacy transactions, supplier shipments, and hospital inventory data for real-time analytics reporting.
Developed scalable ETL workflows using PySpark, Databricks, Delta Lake, and dbt to clean pharmaceutical datasets, standardize product records, and support AI-ready healthcare business data models.
Worked on Gen AI integration using LangChain, Azure OpenAI, Pinecone, and RAG architecture to generate supply shortage insights, shipment delay summaries, and AI-powered healthcare operational responses.
Created Snowflake analytical datasets and AI-powered SQL query workflows using FastAPI, LangChain SQL agents, and OpenAI models, helping business teams access pharmaceutical reports through natural language queries.
Managed workflow orchestration using Apache Airflow, Docker, Kubernetes, and Terraform to automate healthcare ETL jobs, AI document indexing, model refresh processes, and secure cloud infrastructure deployment.
Improved healthcare data quality and governance using Great Expectations, OpenMetadata, AWS IAM, and HIPAA-compliant security controls, reducing reporting inconsistencies and supporting reliable Gen AI healthcare analytics workflows.

Built enterprise healthcare data pipelines using Snowflake, Azure Data Factory, Salesforce APIs, Python, and Apache Airflow to integrate multi-source EHR and CRM datasets with reliable daily refresh processing for analytics teams., Developed scalable ETL and dimensional data models using dbt, SQL, Snowflake, PySpark, and Salesforce data integration workflows, improving healthcare reporting performance and supporting business intelligence dashboards for operational decision-making., Designed RAG-based Gen AI healthcare search platform using LangChain, Azure OpenAI, FAISS, and Pinecone, enabling business users to retrieve clinical and operational insights through natural language queries., Engineered LLM-powered SQL generation workflows using FastAPI, OpenAI APIs, Snowflake Cortex, and LangChain agents, reducing manual reporting dependency and improving self-service healthcare analytics adoption., Implemented AI-driven healthcare data quality monitoring using Python, Scikit-learn, Great Expectations, and Databricks, identifying real-time anomalies before impacting downstream reporting and analytics workflows., Automated cloud-native orchestration and deployment processes using Docker, Kubernetes, Terraform, and Azure services, improving scalability, monitoring, and secure HIPAA-compliant AI data platform operations.

Built enterprise healthcare data pipelines using Snowflake, Azure Data Factory, Salesforce APIs, Python, and Apache Airflow to integrate multi-source EHR and CRM datasets with reliable daily refresh processing for analytics teams.
Developed scalable ETL and dimensional data models using dbt, SQL, Snowflake, PySpark, and Salesforce data integration workflows, improving healthcare reporting performance and supporting business intelligence dashboards for operational decision-making.
Designed RAG-based Gen AI healthcare search platform using LangChain, Azure OpenAI, FAISS, and Pinecone, enabling business users to retrieve clinical and operational insights through natural language queries.
Engineered LLM-powered SQL generation workflows using FastAPI, OpenAI APIs, Snowflake Cortex, and LangChain agents, reducing manual reporting dependency and improving self-service healthcare analytics adoption.
Implemented AI-driven healthcare data quality monitoring using Python, Scikit-learn, Great Expectations, and Databricks, identifying real-time anomalies before impacting downstream reporting and analytics workflows.
Automated cloud-native orchestration and deployment processes using Docker, Kubernetes, Terraform, and Azure services, improving scalability, monitoring, and secure HIPAA-compliant AI data platform operations.

Supported real-time fintech data ingestion pipelines using Apache Kafka, Azure Data Factory, Python, and SQL Server to process digital payment transactions and banking reconciliation workflows efficiently., Developed ETL transformation workflows using Apache Spark, PySpark, AWS Glue, and Amazon Redshift to clean customer loan datasets and improve financial reporting data consistency across platforms., Optimized banking transaction validation processes using SQL, Hive, Hadoop HDFS, and Python automation scripts, reducing duplicate payment records and improving reconciliation accuracy by 31% for operations teams., Implemented automated workflow scheduling using Apache Airflow, Jenkins, and Talend ETL to streamline loan data ingestion, customer onboarding processing, and daily financial analytics reporting activities., Analyzed large-scale digital banking and repayment datasets using Tableau, Power BI, Apache Spark, and Azure Synapse Analytics, increasing operational reporting efficiency by 36% through centralized fintech dashboards., Validated financial transaction quality using Python, Pandas, SQL stored procedures, and AWS S3, identifying missing customer records and improving downstream reporting reliability for business analytics workflows., Collaborated with senior data engineering teams using Git, Docker, Apache Sqoop, and cloud-based ETL environments to support scalable banking data warehousing and secure fintech analytical processing systems.

Supported real-time fintech data ingestion pipelines using Apache Kafka, Azure Data Factory, Python, and SQL Server to process digital payment transactions and banking reconciliation workflows efficiently.
Developed ETL transformation workflows using Apache Spark, PySpark, AWS Glue, and Amazon Redshift to clean customer loan datasets and improve financial reporting data consistency across platforms.
Optimized banking transaction validation processes using SQL, Hive, Hadoop HDFS, and Python automation scripts, reducing duplicate payment records and improving reconciliation accuracy by 31% for operations teams.
Implemented automated workflow scheduling using Apache Airflow, Jenkins, and Talend ETL to streamline loan data ingestion, customer onboarding processing, and daily financial analytics reporting activities.
Analyzed large-scale digital banking and repayment datasets using Tableau, Power BI, Apache Spark, and Azure Synapse Analytics, increasing operational reporting efficiency by 36% through centralized fintech dashboards.
Validated financial transaction quality using Python, Pandas, SQL stored procedures, and AWS S3, identifying missing customer records and improving downstream reporting reliability for business analytics workflows.
Collaborated with senior data engineering teams using Git, Docker, Apache Sqoop, and cloud-based ETL environments to support scalable banking data warehousing and secure fintech analytical processing systems.