Jagadeesh N Email & Phone Number

Texas, United States

Kansas City Metropolitan Area

As a seasoned Data Engineer specializing in healthcare data management and analytics, expertise lies in architecting and implementing intricate AWS cloud solutions tailored specifically for the unique challenges of managing Electronic Health Records (EHR) and HL7 messages. Over 8 years of dedicated experience, a comprehensive understanding of the complexities involved in processing and analyzing healthcare data, leveraging the full capabilities of AWS services to drive transformative advancements within the industry.A cornerstone of the technical approach involves the implementation of real-time data streaming architectures using Amazon Kinesis. By architecting robust data ingestion pipelines and employing advanced data processing techniques, ensures the timely and accurate processing of HL7 messages from diverse sources. This enables healthcare organizations to derive actionable insights in real-time, facilitating prompt interventions and informed decision-making in patient care.In parallel, spearheaded the integration of machine learning models using AWS SageMaker to analyze EHR data. Leveraging sophisticated algorithms and scalable infrastructure, these models enable predictive analytics and personalized medicine applications, empowering healthcare professionals with data-driven insights to enhance clinical outcomes and optimize patient care pathways.Ensuring the utmost compliance and security of healthcare data is a paramount consideration in the technical approach. Meticulously implement AWS Identity and Access Management (IAM) policies and Key Management Service (KMS) encryption to enforce granular access controls and data encryption, safeguarding sensitive patient information against unauthorized access. Continuous monitoring and optimization of AWS infrastructure performance and cost-effectiveness through advanced monitoring tools such as CloudWatch and CloudTrail further reinforce data security measures while optimizing operational efficiency.

Coppell, Texas, United States

As a Data Engineer, spearheaded the development and deployment of a sophisticated platform at DTCC's Coppell facility, specifically engineered for real-time processing and analysis of financial data. Employing a microservices architecture, a diverse range of AWS services were meticulously integrated, including Amazon Kinesis for high-throughput data ingestion, Amazon EMR for distributed data processing utilizing Apache Spark, and Amazon S3 for scalable data storage.Core responsibilities encompassed the intricate implementation of data pipelines seamlessly integrating specialized algorithms tailored for complex event processing, machine learning models, and advanced data mining techniques, all meticulously crafted to optimize financial data analysis. Noteworthy achievements include architecting and optimizing the ETL framework, AWS Glue for automated ETL processes.Ensuring robust data security and compliance measures was paramount, leading to the seamless integration of the ETL framework with DTCC's DLP and data tokenization services. The igration of complex datasets from legacy on-premises storage systems to cloud-based solutions such as Amazon S3 and Amazon Redshift, was executed with meticulous attention to detail to ensure data consistency and integrity throughout the transition process within the LORIS workstream.Uilization of AWS Lambda functions to orchestrate data migration from Amazon Redshift to downstream applications, alongside the use of PySpark scripts for intricate data transformation and manipulation, was pivotal. Playing a central role in implementing and managing ETL solutions using AWS Glue.This involved developing and maintaining AWS Glue and AWS Data pipelines, integrated with AWS CloudWatch and AWS X-Ray for c monitoring and performance optimization, resulting in actionable insights driving informed decision-making.

Englewood, Colorado, United States

As a Data Engineer, pivotal contributions were made in migrating and modernizing Travelport's travel data platform to the Azure cloud. The focus remained on architecting scalable infrastructure and implementing robust data governance measures. The endeavor encompassed the migration of intricate data pipelines, ETL processes, and analytical workloads to Azure services such as Azure Data Factory, Databricks, and Synapse Analytics. To ensure compliance with GDPR and PCI-DSS regulations, stringent data governance and security measures were meticulously implemented, with Azure Monitor and Log Analytics serving as key monitoring and surveillance tools.Technical advisory services were provided to stakeholders, offering design recommendations, resolving technical issues, and devising solutions for Big Data analytics. A significant aspect of the role involved spearheading collaborative efforts to design and implement a comprehensive ETL solution for managing diverse travel data types. Advanced ETL techniques, including SQL and SnowSQL, were utilized to efficiently extract, transform, and load vast volumes of structured and semi-structured travel data, crucial for Travelport's operational and strategic decision-making.The development and deployment of a scalable and secure Azure data platform were meticulously planned and executed, incorporating robust ETL processes using Azure Data Factory, Azure Data Lake, and Azure Synapse Analytics. This empowered Travelport to effectively utilize its data assets, driving enhanced business outcomes and sustaining competitiveness in the travel industry. From a technical standpoint, engineering and optimization of SQL-based ETL pipelines were executed utilizing SnowSQL, Azure Data Factory, and other pertinent technologies, ensuring seamless integration of approximately 150 billion raw records from multiple sources. Furthermore, the architecture and implementation of medium to large-scale Business Intelligence (BI) solutions on Azure.

Boise, Idaho, United States

As a seasoned data professional, my expertise spans end-to-end ETL development and Business Intelligence (BI) solutions. Proficiency in crafting efficient ETL frameworks is showcased, with a robust system in SSIS specifically designed and implemented for processing and monitoring flat file imports, ensuring reliable data management for key clients.In the realm of BI, skilled architecture is evident, adept at designing, building, and deploying impactful solutions using Power BI to enhance data visualization and deliver valuable business insights. Additionally, a strong foundation in version control is demonstrated through leading branching, merging, tagging, and release activities using GIT, streamlining development processes.Automation capabilities are a cornerstone of my skill set, exemplified in the creation of scripts for an in-house metadata explorer project. Through the utilization of Shell Script and SQL, processes are automated, and multiple Metadata resources are loaded from the data warehouse, improving efficiency and accuracy in data management.Furthermore, proficiency in scripting languages like Shell and Python is displayed, with Python scripts developed for seamless data transfer from MongoDB to SQL Database. Additionally, a keen understanding of database optimization is showcased, with various indexing strategies and aggregate techniques implemented to enhance performance. Expertise extends to the management of Analysis Services objects, where strategic configurations are made for optimal functionality. Overall, this multifaceted skill set ensures a holistic approach to data management and analytics, covering ETL, BI, scripting, and database optimization.