Meet Nirav Zaveri

Worked on projects involving Azure Databricks, REST APIs, Python, SQL, and FastAPI to build scalable data pipelines and automation workflows. Explored data warehousing, ETL design, and cloud data engineering to develop efficient analytics and BI solutions.

• Supervised and directed a team of building staff, ensuring seamless daily operations and fostering a safe, inclusive environment for over 200+ patrons daily • Managed staff schedules, rotations, and training sessions, optimizing resource allocation and promoting team leadership growth • Implemented and refined incident response protocols, minimizing disruptions and ensuring timely resolution of issues, contributing to a 95% incident resolution rate • Analyzed facility usage counts and peak hours using data collected via Google Forms and processed in Excel, alongside maintenance reports, resulting in a 15% improvement in space utilization during peak hours and a 10% reduction in equipment downtime through proactive maintenance recommendations • Maintained detailed inventory of sporting goods and gym equipment, achieving a 97% inventory accuracy rate and minimizing equipment shortages

As a Data Engineer, I was pivotal in architecting, developing, and optimizing scalable ETL/ELT pipelines and data solutions, directly translating complex data into strategic business intelligence and enhanced operational efficiency. - Engineered comprehensive ETL pipelines for 3+ critical revenue management applications, integrating diverse datasets (e.g., orders, scans, postal activities). My designs included robust data modeling for data warehousing and complex transformations, resulting in a 20% improvement in Postal revenue KPI reporting accuracy, enabling faster, data-driven decisions on program performance. - Designed and implemented end-to-end CI/CD pipelines using Git, Bitbucket, and Jenkins for core data products. This automation reduced deployment times by 70% and proactively eliminated $80,000/month in revenue leakage for a Package Scanning Interoperability project by ensuring rapid, error-free updates. Fostered seamless cross-functional collaboration. - Optimized large-scale ETL workflows for fleet data using Apache Airflow for orchestration and Informatica for transformations. This initiative slashed refresh times for 2+ Tableau dashboards from 2 hours to just 10 minutes, significantly enhancing data accessibility, analytics scalability, and enabling near real-time operational visibility. - Conducted advanced performance tuning on Teradata SQL queries and BTEQ scripts, addressing bottlenecks in processing over 1.5TB of daily operational and financial data. Through strategic indexing, query rewrites, and partitioning, I achieved a 30% increase in query efficiency, drastically reducing execution times for executive dashboards. - Led a successful Proof of Concept (PoC) for migrating key Teradata data products to Google BigQuery on GCP. This involved complex schema conversions and data transfer validation, achieving a 97% data integrity success rate, influencing strategic cloud adoption.

As an Associate Engineer, I focused on building and fortifying foundational data infrastructure, emphasizing data quality, operational resilience, and secure data delivery to empower downstream analytics and reporting across diverse business domains. - Designed and implemented robust data quality frameworks using UNIX shell scripts and sophisticated SQL-based validation rules, including checks for nulls, uniqueness, data formats, and cross-system consistency. This proactive approach led to a 98% reduction in ingestion errors across 20+ critical data pipelines, significantly streamlining root cause analysis and improving overall data reliability. - Automated comprehensive batch monitoring and incident alerting systems using Python scripts integrated with Splunk for over 100 high-volume SQL workflows, processing more than 500M records daily. This significantly improved anomaly detection speed by 85%, enabling proactive issue resolution and ensuring 99.9% SLA adherence for critical business operations by minimizing downtime. - Provided critical operational support for core data pipelines and analytics layers, efficiently resolving over 80 high-severity data incidents across diverse HR, payroll, and postal domains. - Spearheaded root cause analysis efforts and implemented preventive measures, consistently maintaining 99.5% data integrity across QlikView and SAP Business Objects reports, ensuring uninterrupted access to reliable business insights. - Enforced GDPR-compliant data lifecycle management policies by developing and implementing structured backup, archival, and data retention routines. This initiative not only reduced potential system downtime by 15% but also ensured meticulous audit readiness and enhanced Personally Identifiable Information (PII) data protection for sensitive organizational data.

Completed comprehensive coursework including: - Database Foundations for Business Analytics - Professional Development - Object-Oriented Programming with Python - Advanced Statistics for Data Science - Predictive Analytics for Data Science - Big Data - Systems Analysis and Project Management - Business Analytics with R - Applied Machine Learning - Business Data Warehousing - Spreadsheet Modeling and Analytics - Digital Consulting Project - Marketing Management Earned a Graduate Certificate in Business Analytics and Data Mining. Focused on building expertise in data engineering, analytics, machine learning, and cloud-based data systems through academic projects and applied coursework.