Tabrez Shah

Worked at the intersection of Data Engineering and Analytics, supporting Uber's advertising and measurement ecosystem through scalable data solutions and business insights. Designed and maintained automated data pipelines for 14+ third-party measurement partners including Criteo, Kantar, Comscore, LiveRamp, and Happydemics, ensuring SLA-compliant and reliable data delivery. Built self-service dashboards and real-time monitoring solutions using SQL, Python, and Grafana to improve visibility into campaign performance, data quality, and pipeline health. Conducted audience segmentation, market basket analysis, campaign measurement, and location intelligence studies to optimize targeting strategies and improve advertising ROI. Collaborated with global stakeholders and engineering teams across multiple regions to support data integrations, international launches, reporting automation, and data-driven decision making at scale.

Worked as a Data Engineer supporting logistics and supply chain analytics by building scalable data pipelines, optimizing reporting workflows, and enabling data-driven decision-making. Developed and maintained ETL pipelines to ingest and transform data from multiple operational systems while leading the migration of on-premise data to AWS S3, reducing reporting delays and improving data accessibility. Built centralized datasets from shipment, order, and delivery data to support KPI tracking and operational reporting. Optimized SQL queries and data workflows, improving processing efficiency and dashboard performance. Performed data validation and quality checks to enhance data accuracy and reliability across logistics datasets. Developed Tableau dashboards to monitor delivery timelines, order volumes, operational performance, and key business metrics. Partnered with business stakeholders to identify operational bottlenecks and deliver actionable insights that improved decision-making and overall process efficiency.

Collected, cleaned, and analyzed multi-source datasets to ensure accuracy and reliability
Performed exploratory data analysis to identify trends and support business decisions
Developed SQL and Python-based reporting pipelines, improving reporting efficiency by 20%
Built Tableau dashboards for KPI tracking and performance monitoring
Improved data accuracy by 25% through validation and discrepancy resolution
Delivered end-to-end reporting solutions (data extraction → transformation →visualization) for client use cases