Successfully spearheaded the architect teams to solve the strategy/vision to complete a phase of largest cloud migration of Health Care Company.• Worked on Data Catalog and Data governance using Alation.• Solutioning Enterprise Data Lake platform on cloud environment • Analyzed the data by querying the Dataset using python (panda, NumPy library) using google vertex AI.• Build Enterprise data Platform to migrate on-prem Hadoop ecosystem to the GCP.• Design Data proc/Composer/Airflow data pipeline on google platform.• Designed and mapped source to target mapping across the various sources such Medallia, Amwell, Epic to the Target system for satisfying the Value based care KIPS and build the Executive dashboard for the Business user.• Build the Row level security model in the Bigdata platform.• Build Cloud enable Datawarehouse platform using GCP Big query and Snowflake.• Solutioning Data cataloging to derive the data lineage and better data governance using Allation in google cloud platform.• Integrate Tableau with data catalog tool as Alation to provide the security data governance platform.• Evaluated and Implemented Cloud-enabled BI Solution on GCP framework.

· Worked on of Data Catalog and Data governance using Informatica EDC, AXON, Globalid· Design and developed Data model for BFSI client data model. · Build Data stewardship use cases and defined the different sources of the data and derived the lineage.· Build the snow pipe to load the data from the S3 where Different 3rd party marketing dataset provisioned in different bucket.· Build the Marketing DW framework on cloud using Snowflake DW platform.· Gathered kpis and developed the Tableau dash board across all the rating type such as moody , S&P etc.

· Build the capability of Nifi Based data pipelining to process Claims data (provider/Payers) on AWS platform.· Designed and developed Logging framework (using ELK).· Provided strategic solution for Novartis Cloud data warehousing using Redshift.· Design Analytics platform by Deploying Kylin cube in collaboration with Kyligence team on the AWS platform for one Cigna data lake. · Analyze the members Data associated with the provider and derive the data nuances· Build data Catalog by using Panda and NumPy library. · Developed Data driven ML and rule engine on ERISA data.· Enable Data governance using Apache Atlas and Ranger.· Build Provider data model on HL7/CDA Data files.· Build the CDR data model and Enabled FHIR API.· Build the DQ check while processing the CDA file.· Build patient 360 data model using CCDA file. · Engage and motivating various healthcare clients on Blockchain use cases and its implementation.

Designed metadata rule engine to process last 4 years of health care claims data.· Designed and developed Spark packages which provide a standard training dataset which feeds AWS storage for predictive models.· Designed and developed the interactive Tableau dashboard . · Wrote Hive HQL for quality assurance before persisting data in Hadoop layer. · Data analysis using spark (pyspark)

• Build entire Data lake on Azure Cloud (Hortonworks flavor) • Designed Apache Kylin framework to optimize query turnaround time from minutes to milliseconds.• Designed data-ingestion framework for data lake• Facilitated data engineering for several population health analytics reports.• Integrated Hive & HBase (Phoenix) to process huge volumes of Payer data.• Security using Apache Ranger• Managed ETL team and guided Pig, Oozie & Shell scripting.• Proof of concepts on Apache Presto and building Mulesoft APIs• Multiple linear regression, SVM, Random Forest using R• Critical analysis and decision making on various solutions of big data methodologies including SAP HANA, Spark, Hadoop, HBase, Cassandra, AWS/Azure, Kafka, Oracle DB and ETL tools• Built 11 node Hadoop Cluster on AWS• Designed Storm parser code to ETL from Kafka to Cassandra; wrote sqoop script for data ingestion from various sources.• Built systems for live alerts, real time tracking & reporting, predictive analysis based on real time and historical data & batch reporting.• Improved overall efficiency and performance in database design, architecture and data flow.