Shrey Modi

• Gathered requirements from Finance, Operations, and Project Management stakeholders and translated them into BRD/FRD
specifications, delivering an executive Power BI reporting ecosystem used by 20+ senior leaders for real-time visibility into
project KPIs, financial performance, and labor productivity.
• Designed and maintained Power BI semantic models integrating SAP Business One labor, cost, contract, and progress data from
field, improving reporting accuracy by 30% and establishing a single source of truth for business decision-making.
• Defined project KPI frameworks and performed budget-variance analysis, forecasting, and CPI/PF productivity analysis, enabling
teams to identify performance risk earlier and improve forecast reliability.
• Built a predictive project analytics application that forecasted lump-sum project overruns and identified early risk indicators,
improving forecast accuracy and supporting proactive cost-control decisions.
• Automated SQL-based reporting and SAP B1 extraction workflows, reducing reporting turnaround by 15% and saving 5-10 hours
of manual effort per week.
• Developed Power Apps solutions for timesheet entry and labor tracking, reducing data-entry errors by 20% and improving field
to-office reporting efficiency.
• Collaborated with Finance, Operations, and Project Management stakeholders to gather requirements, validate data quality,
and deliver analytics solutions that improved reporting adoption and decision-making effectiveness.

• Built and automated interactive KPI dashboards for marketing and operations teams, reducing manual reporting effort by 50%
and improving visibility into customer behavior and campaign performance.
• Performed data cleansing, transformation, and validation in SQL and Python, improving data accuracy by 25% and ensuring
reliable datasets for stakeholder reporting.
• Integrated third-party APIs with Google Sheets via JavaScript, eliminating manual extraction and reducing reporting turnaround
by 80% for near real-time access to business metrics.

• Enhanced CI/CD pipelines using GitHub Actions and Docker, reducing build times by 40% and deployment failures by 30%,
improving reliability of data and machine-learning workflows.
• Provisioned AWS infrastructure (Lambda, EC2, S3, ECR, ECS) to support scalable data-processing and machine-learning
workloads against a 99.9% uptime target.
• Optimized Python-based data-processing pipelines, increasing throughput by 50% and reducing latency by 25%, and
documented data flows and integration points to support analytics and model development.