Harshitha Chitla Email & Phone Number

Boston, Massachusetts, United States

•Managed all stages of the Software Development Life Cycle (SDLC), including requirements analysis, technical design, coding, testing, and deployment, ensuring effective solutions for insurance data management.•Designed and implemented scalable ETL pipelines using Google Cloud Platform (GCP) services such as BigQuery and Cloud Storage to manage large insurance datasets.•Developed real-time data streaming solutions with Google Cloud Pub/Sub, enhancing data processing and integration for insurance claims and underwriting systems.•Utilized Dataflow automated templates for seamless data management between Google Cloud Storage (GCS) and BigQuery, improving data pipeline efficiency.•Using Talend, end-to-end ETL processes were designed and created, simplifying the integration of data from various sources into data warehouses and analytics platforms.•Experienced in Talend jobs were configured and optimized for data transformation, guaranteeing good processing performance and scalability even for big datasets.•Created and maintained complex data models and data warehouses in BigQuery to support analytics and business intelligence for insurance metrics.•Implemented automated ETL processes with Apache Airflow and Python scripts, optimizing ETL workflows and ensuring data integrity.•Integrated insurance data from several sources, such as policy administration systems and claims databases, into cloud-based data warehouses like Snowflake and Redshift, we designed and constructed ETL pipelines utilizing Matillion.•Collaborated with cross-functional teams using JIRA and Confluence to align data engineering efforts with underwriting, claims, and finance needs.•Executed AWS Glue tasks that optimized to extract, transform, and load data and insurance policy data into data lakes and Amazon Redshift.•Utilized Power BI for data insights and forecasting, analyzing insurance metrics, claims frequency.

Rochester-Austin, Minnesota Area

•Engineered a centralized Data Lake for clinic operations using AWS S3, EMR, Redshift, and Athena, enhancing data processing, storage, and reporting of large-scale healthcare datasets.•Executed data cleaning and transformation operations to maintain high-quality clinical reference data, collaborating with stakeholders and solution architects.•Developed ETL pipelines with Spark SQL in Databricks, efficiently processing and aggregating healthcare data from diverse sources and file formats.•Created detailed reporting tools with Oracle PL/SQL for complex data analysis, supporting clinical data management and decision-making.•Utilized Docker and Kubernetes for reproducible and scalable data processing environments, optimizing clinic data workflows.•Automated repetitive tasks using Oracle PL/SQL, improving operational efficiency in healthcare data management.•Designed Star and Snowflake Schema Data Models using ERWIN to support efficient data warehousing and retrieval for clinical data.•Implemented scalable data warehousing solutions with Snowflake, facilitating high-performance storage and analytics of clinical data.•Enhanced MongoDB performance through indexing and managing database operations, improving clinic data storage and accessibility.•Automated cloud infrastructure provisioning with Terraform integrated into CI/CD pipelines, ensuring consistent deployment of clinic data environments.•Migrated and managed healthcare databases, including PostgreSQL and other platforms, for effective clinic data management and conversion.•Applied machine learning models in GCP using TensorFlow and AI Platform to analyze patient outcomes and predict treatment success, enhancing clinical data insights and decision-making.

Lake Success, New York, United States

• Extensive experience with the Hadoop ecosystem and Azure services, including Synapse, Stream Analytics, Event Hubs, and Event Grid, for real-time data processing and analytics.• Migrated SQL databases to Hadoop-based platforms like Azure Data Lake, SQL Database, and SQL Data Warehouse, ensuring seamless transitions and optimized performance.• Developed and managed end-to-end data workflows using Azure Data Factory and Hadoop tools, covering data reception, validation, transformation, and publishing.• Recreated and migrated legacy application logic to Hadoop environments, including Azure Data Factory, Data Lake, and SQL Data Warehouse, ensuring continuity and functionality.• Utilized Snowflake for cloud-based data warehousing, integrating it with Hadoop and Azure services, and implemented security features like RBAC and data encryption.• Created and optimized collaborative notebooks in Azure Databricks using Python, Scala, and SQL for data analysis, visualization, and reporting.• Developed ETL scripts and pipelines using Node.js within Hadoop workflows, and optimized complex SQL queries in Snowflake for advanced analytics.• Automated cloud infrastructure deployment using Terraform, reducing deployment times by 50% and ensuring consistent environments across Hadoop ecosystems.• Integrated Azure Data Factory with Hadoop services, including Data Lake, SQL Database, and other Azure tools, to streamline data workflows and analytics.• Led data migration projects in Hadoop environments, leveraging tools like GitHub, Jenkins, and SQOOP, and created visualizations with Tableau.

Hyderabad, Telangana, India

▪Utilized Informatica Repository Manager to oversee and manage repositories, including development, testing, and validation. Additionally, participated in the transfer of folders between different repositories.▪Engaged in the migration of PowerCenter folders from the Development to the Production Repository using Repository Manager.▪Created a comprehensive Unit Test Document and conducted data loads, ensuring the reliability of ETL processes.▪Proficient in technologies like HP UNIX, Oracle, SQL, and PL/SQL, I contributed significantly to the seamless processing of ACH data, maintaining data accuracy across environments.▪Utilized ERWIN Data Modeler to design and maintain data models, ensuring alignment with business requirements and data governance standards throughout the development lifecycle.▪Created a Unit Test Document that outlines test conditions, scripts, and expected/actual results.▪Executed data loads across environments utilizing DAC (Data Warehouse Application Console).▪Conducted comprehensive testing by creating QA procedures to verify target data against source data.▪Prepared design documents for each ETL mapping, outlining source and target tables, fields, transformations, and join conditions to enhance user understanding of the data.▪Implemented ETL procedures for processing ACH data, integrating it from diverse sources into analytical databases and data warehouses.▪Conducted meticulous data cleaning and validation procedures to maintain the accuracy and integrity of ACH data, supporting reliable data analysis and reporting.▪Ensured the accuracy and integrity of ACH data through meticulous data cleaning and validation procedures.▪Analyzed DB discrepancies and maintained staging, development, UAT, and production environments to ensure consistency across data models.▪Utilized Power BI to generate visual reports, dashboards, and KPI scorecards, enhancing business insights.