Nagnath Dahale

Architected and implemented enterprise-grade lakehouse platform on Google Cloud Platform using Apache Iceberg, Dataproc, BigQuery, and Databricks, processing 65 GB daily data from 35+ heterogeneous sources Designed secure landing zones with multi-layered data architecture implementing bronze, silver, and gold data layers with automated data quality validation and rollback mechanisms Established comprehensive data governance framework using Dataplex for metadata management, data cataloging, lineage tracking, and policy enforcement across 100+ datasets Built and deployed CI/CD pipelines for data engineering workflows using Jenkins, Terraform, and Git, reducing deployment time by 40% and eliminating manual errors Led cross-functional team of 6 data engineers, establishing coding standards, reusable ingestion templates, and best practices that improved development velocity by 35% Implemented Infrastructure as Code (IaC) using Terraform for automated provisioning of GCP resources, ensuring consistency across development, staging, and production environments Designed automated data validation framework with Great Expectations, catching data quality issues before production deployment Optimized BigQuery query performance achieving 45% cost reduction through partition pruning, clustering strategies, and materialized views

Led end-to-end migration of on-premises Hadoop and Spark workloads to Google Cloud Platform (Dataproc and BigQuery), successfully migrating 50+ production pipelines with zero downtime Designed and implemented autoscaling configuration for Dataproc clusters, optimizing resource utilization and reducing infrastructure costs by 15% Architected reusable data pipeline framework using PySpark and Apache Beam, standardizing data ingestion patterns across organization and reducing development time by 30% Implemented comprehensive IAM-based access control policies and VPC configurations ensuring secure data access and compliance with enterprise security standards Built data lineage and cataloging solution using Dataplex integrated with BigQuery, providing end-to-end visibility into data flows across 200+ tables

Reduced average job runtime by 30% through optimization of Spark configurations, partition strategies, and caching mechanisms Developed monitoring and alerting framework using Cloud Monitoring and Cloud Logging for proactive issue detection and resolution Mentored team of 8 junior engineers on GCP best practices, data engineering patterns, and cloud-native development approaches Collaborated with data scientists to build ML pipeline orchestration using Cloud Composer (Apache Airflow), scheduling and monitoring 25+ ML workflows

Developed scalable advertising publishing platform on Google App Engine integrating with Google Ads, Facebook Ads, and Instagram APIs, processing 1M+ ad impressions daily Built real-time data ingestion pipelines using Pub/Sub and Dataflow, enabling near-real-time analytics with sub-minute latency for customer engagement metrics Designed and implemented recommendation engine data pipelines using Apache Beam and Cloud Composer, improving personalization accuracy by 25% Optimized Dataflow streaming jobs through windowing strategies and state management, reducing processing costs by 20% Implemented data quality checks and monitoring for streaming pipelines ensuring 99.9% data accuracy Collaborated with product and analytics teams to design dimensional data models in BigQuery supporting marketing analytics use cases Developed Python-based automation tools for campaign management reducing manual effort by 50%