Paddy Krishnamoorthy
Data, ML Ops & Cloud Architect, Consultant @ Vanguard
- Role
- Data & Ai Architect Consultant at Vanguard
- Location
- Philadelphia, PA, US
- LinkedIn followers
- 500 followers
About Paddy Krishnamoorthy
Big Data architect with hands on experience to architect, acquire, manage and analyze extremely large structured and unstructured data. Ability to quickly learn and implement modern distributed architecture and infrastructure to process unstructured data in near real time. Specialties: Big Data Architect and Analytics, Java, Python, C#, Spark, Hadoop, HDFS, NoSQL, Machine Learning, Text Mining, Natural Language Processing, System Design, Enterprise Security. Domain Experience in Online Media Advertisements, Healthcare Insurance, TV Broadcasting and Life Science industries. Also worked on Spring Framework, WebSphere, Tivoli Access Manager, WebLogic, ORACLE, SQL Server, IBM TxSeries, MQ Series and etc.
Experience
Data & Ai Architect Consultant
Dec 2018 — Present · Malvern, PA
Just completed building one of the largest data lake (within Vanguard) to ingest data from on-perm enterprise databases and other third party sources. The program is to implement a comprehensive end to end data management program comprising of Ingestion, Compose Layer Build out, Data Reconciliation, Data Quality, Data Governance and Access Management. Designed and implemented a framework based solution to provide ACID capacity, scalable metadata management and a unified pipeline for end to end data processing on the cloud. Tools and technology used : AWS EMR, PySpark, CloudFormation, Service Catalog, Glue Catalog, Hive, Collibra, RedPoint, Attunity, Bamboo, Splunk and etc. Salient Features: Configuration based ingestion, a schema centric pipeline, seamless schema evolution, source to target data reconciliation, custom data quality checks, Integration with Collibra DGC Catalog mplemented and operationalized ML CI/CD pipeline to run AWS SageMaker Batch Inference. A PySpark based pipeline includes loading data from S3, transformation it to match Sagemaker input formats, invoke SageMaker Batch inference, perform post transformations and enable the results consumed via Hive and S3. Tools and Technology Used: AWS SageMaker, EMR, S3, CloudFormation, Service Catalog, S3, Bamboo Next, am hoping to engaged in modernization of Monolith systems leveraging Event Streaming, Service Mesh and Micro services.
Education
Penn State Great Valley
MS, Software Engineering
Bangalore University
Bachelors of Software Engineering
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