Anand Prashar

 Over 11 years of experience in designing and implementing a wide range of impactful data driven solutions with a masters in Big Data Informatics

 Has extensive expertise in a wide range of data management aspects like Data Ingestion, Integration,

Warehousing, Data Governance & Compliance, Quality, Modeling, Business Intelligence and more.

 Hands-on technologist with a passion for creating and operationalizing ac actionable data strategy to help drive business value and innovation

• Designed and developed several big data ingestion architectures for analytical & predictive business data platform
• Developed architectures include API based, Flat file based, Stream based, Webhook based, Batch based etc.
• Developed different database types: Datalake(Ingress), Enrichment(modelling) and a range of Publish databases

(Egress)

• Designed and developed the journal and snapshot tables build-up logic scaled for 1000s of data-objects that are regularly population while remaining consistent and true with the data-sources
• Designed and developed frameworks to ensure data governance controlling PII, MNPI, GDPR compliance Publish Dbs that expose or redact row/column level data on user role or IDs
• Designed and developed scalable and extendable pre-processing systems to correct file based ingestion architectures.

  This would result in an extensible automated framework to fix observed ingestion errors in records and re-ingest.

• Developed opensearch based platform logging systems ensuring event and log storages for audits
• Designed and developed Pagerduty based integration, incident classification, escalation policies and alerting
• Created parallelization frameworks for production-deployments to Snowflake data warehouses reducing time by 1/10
• Built data systems to extract, transform and load data from various external and internal endpoints of value
• Built several automated scheduling systems using Airflow using factory design-patterns, thus ensuring the scheduling and automated workflow management for consistent data availability 
• Built Machine Learning base analyzers to understand git code base and map dependencies graphs between database 

tables

• Utilized extracted information to build knowledge base and then to map equivalent products in big data architecture

system to assist in data discovery

• Designed and deployed a robust data quality solution to ensure high data reliability and compliance with SOX requirements. Established data quality solutions and implemented observability metrics to monitor and alert on data inconsistencies, ensuring data reliability and integrity

• Actively worked with multiple stakeholders, and wore multiple hats to deliver the end-to-end product

Technologies: Snowflake, Airflow, Various AWS services, Python, CI/CD, Tungsten, MySQL, Postgres, Snowpipe streaming, Terragrunt, Airbyte, Helm, EKS, Docker

Master of Science Data Informatics  University of Southern California August 2016 –May 2018

Coursework: Data Mining, Machine Learning, Building Knowledge Graphs, Applied Natural Language Processing, Big Data Management (SQL & NoSQL), Professional Practicum, Directed Research

Bachelor of Technology Computer Science and Engineering VIT University July 2008 –May 2012

Coursework: Database Management, Algorithms, Data Structures, Applied Probability & Statistics etc.