Loveleen Kaur

Project: automotiveMastermind Reporting & Analytics Platform. Built and maintained scalable data pipelines and analytics workflows for automotiveMastermind, an automotive analytics product, enabling actionable insights and data-driven decision-making for dealerships. Key Contributions: Established and owned end-to-end Airflow + BigQuery ETL pipelines on GCP, transforming raw data into SSOT datasets for analytics and reporting. Optimized BigQuery performance using partitioning, clustering, and SQL tuning, achieving 40% faster queries and 20% monthly cost reduction. Applied data quality checks, null handling, and conformance rules to improve downstream reporting accuracy and stability. Developed SCD Type 1 & 2 models for dealer hierarchies and campaign attribution to support accurate historical reporting. Designed pre-aggregated Gold tables for campaign and sales metrics, reducing Tableau dashboard latency by 40%. Implemented a fully automated reporting system generating 10K+ weekly dealer reports with 98% SLA, eliminating 15+ hours/week of manual effort. Migrated legacy Go services to Python Cloud Functions, improving pipeline maintainability and processing speed by 30%. Integrated

Assisted in a major BigQuery data migration, improving consistency and cutting retrieval time by 20%. Developed automation scripts and feature enhancements, boosting user satisfaction by 25%. Coordinated Agile tasks to ensure 100% on-time delivery of sprint commitments.