Designed and developed an end-to-end data engineering solution simulating an enterprise automotive manufacturing environment. Implemented a Medallion Architecture (Bronze, Silver, Gold) to support scalable data ingestion, transformation, and analytical processing. Developed ETL pipelines using PySpark and Apache Airflow to orchestrate ingestion, transformation, validation, and warehouse loading processes. Designed dimensional data models (Star Schema) in PostgreSQL to support analytical reporting and manufacturing KPIs. Implemented automated data quality validation and transformation processes to improve consistency and reliability across production datasets. Built analytical datasets for production monitoring, equipment performance, quality inspection, and manufacturing operations. Generated manufacturing KPIs including Overall Equipment Effectiveness (OEE), downtime, production throughput, and defect rate. Applied modern data engineering practices focused on scalability, modularity, and maintainability for analytical workloads.

Duties: Designed and delivered enterprise-scale analytics and automation solutions supporting operational performance, executive decision-making, and service management for multinational clients across North America. Developed scalable data pipelines integrating data from SAP BI, ServiceNow, Azure services, REST APIs, Excel, and relational databases to enable reliable reporting and business analytics. Built and optimized ETL processes, data models, and data validation frameworks to improve data quality, reporting governance, and consistency across multiple business units. Automated business workflows using Python, Microsoft Power Platform, VBA, and Power Automate, reducing manual effort and increasing operational efficiency. Developed interactive Power BI dashboards and analytical solutions to monitor operational KPIs, SLA compliance, service performance, and business trends. Integrated enterprise applications through REST APIs and Postman, supporting secure and reliable data exchange between ServiceNow, Azure, and reporting platforms. Collaborated with cross-functional stakeholders to gather business requirements, define KPIs, and translate operational challenges into scalable data-driven solutions. Supported digital transformation initiatives by integrating analytics, automation, and cloud services into enterprise operational workflows. Accomplishments: Improved SLA compliance by 20% through process automation and reporting optimization. Automated approximately 40% of reporting activities, significantly reducing delivery time and manual errors. Reduced engineering data extraction time by implementing automated ETL integrations using REST APIs and Azure services.

Duties: Coordinated high-priority incident response across global technical teams following ITIL best practices. Leveraged ServiceNow to monitor operational performance, incident trends, and service metrics supporting data-driven decision-making. Performed Root Cause Analysis (RCA) to identify recurring operational issues and recommend continuous improvement initiatives. Collaborated with technical teams and business stakeholders to prioritize incidents, manage escalations, and maintain service continuity. Monitored operational KPIs and generated analytical insights to improve incident response efficiency and process performance. Accomplishments: Improved incident response efficiency by 15% through optimized monitoring, escalation workflows, and cross-functional coordination. Led post-incident retrospectives to document root causes, resolutions, and lessons learned, reducing recurrence and improving future resolution times. Advanced into a Data Analyst & Automation Specialist role through demonstrated expertise in process optimization, data-driven problem-solving, and operational analytics.