Vishal Dixit

Managed and optimized AWS DMS replication tasks; migrated selected workloads to Zero-ETL pipelines, reducing data movement costs by 30% while maintaining near-real-time availability in Redshift. Built and maintained scalable ETL pipelines processing 5–20 GB/day using AWS Glue (Spark) and Python to load batch and incremental data into S3/Redshift, with monitoring, retries, and freshness validation. Designed data models and ingestion workflows integrating CRM, transactional, and agronomy API data into a unified operational dataset consumed by internal dashboards and business workflows. Built RabbitMQ-based event ingestion services to capture operational transactions with idempotent consumers and retry handling, ensuring reliable and exactly-once processing.

Built Python-based data processing pipelines to analyze large-scale SoC design metrics and logs across multiple engineering teams. Automated SoC design workflows (lint, simulation, synthesis) using Python scripting, reducing manual engineering effort by 60–70% and accelerating design iteration cycles. Developed internal analytics dashboards and reporting workflows in Power BI to support engineering decision-making across design and verification teams.