Mayur is a Data Architect and Engineer with 17 years of IT experience managing and supporting business process-led technology and strategic management initiatives. He builds products from scratch, developing PoCs for CXOs and converting them to production-grade solutions. With a strong analytical background, Mayur is a high-caliber Big Data/Data Warehouse/ETL architect with expertise in data management focused on Big Data, EDW, Cloud Data Warehouse, real-time analytics, and data lakes. He also boasts good balance of technical and management skills and a proven ability to lead large, complex projects and globally distributed teams.
Planning, conceiving, and developing a project to switch from UKG-managed Cassandra clusters to a fully managed database as a service.
Migrating 45+ Cassandra clusters to a fully managed service with zero downtime to customers; saved ~$22 million by reducing the TCO of Cassandra by 1/8th in the organization.
Planning the architectural runway to support new business features and capabilities, establishing serviceability and observability of the application.
Worked on various client projects implementing metadata management, data warehouse modernization, ETL, syntax and data validation services.
Designed and developed various features: schema translation and target-based optimization DML/code transformation, ETL to PySnowSQL/PySpark automated translation, and translation support for major modern DWs.
Reduced the operating cost of a modernization project by 80% with AI-powered frameworks.
Architected and designed a Big Data cluster provisioning tool with core framework for deployment, including provisioning for Hadoop and its ecosystem components like Ganglia, Kafka, Storm, Oozie, Zookeeper.
Implemented monitoring metrics using Ganglia, Prometheus and developed log, service management, and auditing of properties.
Owned end-to-end deployment of proprietary software on HDP and CDH clusters across clouds - AWS, Azure.
Developed a Metadata Management and Governance Tool featuring a Metadata Catalog, an automated metadata crawler, data observability/quality profiler and data/cross-system lineage for impact analysis.
Collaborated with various teams, liaised with stakeholders, consulted with customers, used industry trends knowledge to guarantee data security.
Employed technologies including Spark, Spark Graphx/Graphframes, Java, Spring Boot, Apache Ranger, Antlr, and Solr.
Implemented EDW Code and Query Log Analysis feature, technical debt and dead code detection, ML-based Query time prediction in Spark, Snowflake, Redshift.
Developed features for DW Migration project estimation, target compatibility matrix, SaaS based cloud deployments for assessments, and customized offerings for AWS, Azure, GCP partners.
Reduced analysis time by 70% by re-platforming product on Databricks and Snowflake.
