Sumit Kumar Mishra

Managed a 500 GB enterprise data warehouse on GCP, ensuring high availability and performance for a leading retail client's critical data assets.

Engineered and deployed a comprehensive data processing pipeline using GCP Dataflow, Pub/Sub, and BigQuery, supporting data ingestion and transformation for a Fortune 500 Retail client.

Developed a propensity model leveraging GA4 event data and BQML logistic regression, enabling precise audience targeting in Google Ads and enhancing marketing effectiveness.

Automated data ingestion from MikMak into BigQuery via a Cloud Run Job, eliminating manual processes and achieving significant operational cost reductions by six figures.

Led a cost-saving initiative by identifying unused GCP resources and establishing bucket expiration policies, resulting in an annual cost reduction exceeding $50,000.

Implemented robust CI/CD pipelines on GCP using Terraform, Cloud Build, and GitHub, automating infrastructure provisioning, testing, and deployment across multiple environments.

Implemented a Medallion-based data architecture utilizing Apache Airflow and dbt, standardizing data quality and accessibility across various layers.

Designed and deployed a scalable ETL framework using Apache Airflow and Spark, efficiently processing 5TB of daily data from multiple diverse sources.

Built and deployed a Vertex AI pipeline to automate unstructured document processing, significantly reducing manual data extraction time and improving operational efficiency.

Optimized BigQuery performance through strategic partitioning, clustering, and efficient join strategies, leading to substantial reductions in query execution time and cost.

Developed and maintained robust SQL pipelines in BigQuery, transforming silver/gold models to power self-service Looker dashboards for enhanced business intelligence.

Implemented an ETL process that streamlined data import from 20+ diverse sources into BigQuery, enhancing data consolidation and accessibility.

Designed and developed robust ETL pipelines leveraging Google Cloud Composer and BigQuery for efficient data orchestration and warehousing.

Optimized SQL queries and database indexing, leading to significant improvements in query performance and a measurable reduction in server load.

Developed and maintained Python scripts for data cleansing and transformation, significantly improving data quality and reducing manual data processing time by 20 hours per week.

Implemented complex data processing tasks in BigQuery, efficiently handling datasets exceeding 1TB to support large-scale analytics.

Built and deployed interactive dashboards using Tableau and Data Studio, providing critical insights for data-driven decision-making across the organization.