Sourav Maity

Architected scalable, analytics-ready GCP ETL pipelines using PySpark and Python, transforming multi-terabyte financial datasets into curated BigQuery tables optimized for enterprise dashboards, reporting, and ad-hoc analytics across 10+ business teams. Developed performance-tuned dimensional data models using advanced SQL and PL/SQL-style logic (CTEs, window functions, stored procedures), supporting delta loads and SCD Type-2 historization, resulting in ~30% improvement in data accuracy and reporting consistency. Automated end-to-end GCP data workflows using Apache Airflow (Cloud Composer), integrating GitLab CI/CD to orchestrate BigQuery pipelines, reducing manual operational effort by ~60% and improving reliability of analytics data delivery. Optimized ingestion and transformation logic through parallel processing and multithreading, achieving 35-45% reduction in pipeline runtimes and improving data freshness SLAs for downstream dashboards and reports. Led and coordinated mainframe-to-cloud data migration initiatives on GCP, converting business requirements into technical solutions, reviewing deliverables, validating data quality and schema parity, and ensuring on-time delivery of modernized BigQuery-based reporting platforms.

Designed, developed, and maintained interactive Power BI dashboards and reporting infrastructure using Azure Synapse, delivering real-time visibility across 50+ business KPIs and improving data-driven decision-making by ~40%. Led user segmentation, cohort, and behavioral analysis for a 3M+ user base, driving 85% improvement in user retention and enabling targeted product and marketing strategies. Built and operationalized analytics pipelines and dashboards using Python-based REST API integrations and GCP services, enabling automated ingestion of application event data and reducing marketing campaign costs by ~65%. Implemented web and keyword analytics reporting frameworks to support SEO and growth initiatives, increasing organic traffic by ~25% and generating 15% more qualified leads.

Analyzed sales and lead lifecycle data using SQL-based reporting and process mining techniques, improving the Lead-to-Pay conversion ratio by ~15% through identification of operational bottlenecks. Automated recurring sales and performance reports by building SQL-driven data pipelines on AWS, reducing manual reporting effort by ~60% and improving report turnaround time. Performed cohort and RFM-based segmentation analysis using SQL to identify pricing and discount optimization opportunities, contributing to ~25% revenue growth in the first quarter.

Analyzed large-scale transactional and order-level data by leveraging AWS Lambda for automated data processing and Redshift for high-performance SQL querying to identify fraud patterns and risk signals across the Amazon platform. Detected AML risk trends within high-value order segments, enabling preventive controls that avoided estimated losses of $30K+ per month. Owned the creation of high-value order risk segmentation logic, supporting faster risk review workflows and improving effectiveness of fraud mitigation actions.