Mohammed Arshad

Collaborated across Security, Engineering, and Product teams in Agile sprints to define KPIs and translate business needs into analytical frameworks. Optimized complex SQL queries and Python (Pandas/NumPy) scripts for infrastructure performance analytics, reducing average query execution time by 35% via indexing and refactoring. Engineered scalable AWS Glue and S3 pipelines to ingest and process 50M+ daily network traffic and security log records for downstream reporting. Built 10+ Tableau dashboards tracking DDoS mitigation, latency trends, and CDN performance. Applied scikit-learn classification/anomaly detection to identify irregular traffic patterns, improving early threat detection accuracy by 20%. Automated recurring operational reporting using Python with AWS RDS to cut manual report generation time by 45%. Conducted hypothesis testing and regression analysis across 200+ global data centers to inform a network optimization initiative targeting 15% latency reduction.

Developed and deployed 8+ Power BI dashboards using Power Query and DAX to track delivery timelines, resource utilization, and SLA compliance—improving stakeholder reporting turnaround by 50%. Wrote and optimized SQL queries across MySQL and PostgreSQL to extract, transform, and load operational data for 15+ client accounts supporting weekly and monthly BI cycles. Built ETL workflows using Python (Pandas) and Excel (VLOOKUP/XLOOKUP, pivots) to consolidate disparate sources, reducing reconciliation errors by 30%. Performed EDA and statistical analysis on project cost and delivery metrics, identifying inefficiencies that reduced average project overrun rates by 20%. Partnered with project managers and client-facing teams to gather reporting requirements and automate solutions, eliminating 12+ hours of manual reporting per week. Implemented data quality validation checks and cleaning scripts on relational databases holding 2M+ records, improving data integrity scores by 25%.