Priyam Kumar Bajaj

Engineered semantic models and governed enterprise-wide KPIs (utilization, desk efficiency) to ensure data consistency and to serve as foundational knowledge for internal AI agents. Built and validated AI-embedded data apps connected to Hive data sources, implementing QA frameworks for LLM outputs to deliver highly accurate, actionable insights. Translated internal customer sentiment into quantifiable CX metrics (satisfaction indices, composite scores) to help operational leaders improve end-user workplace experience. Accelerated analytics development using Claude Code to prototype dashboard logic and to write/debug SQL pipelines, reducing multi-day cycles to hours. Led enterprise BI initiatives for Global Workplace Planning, owning end-to-end pipeline design and delivering Tableau dashboards for executive business reviews.

Architected a modern data warehouse and semantic layer using SQL, BigQuery, and dbt, establishing standardized metrics and enabling self-serve analytics company-wide. Identified early churn signals through behavioral pattern analysis and surfaced actionable insights that drove proactive interventions for at-risk segments. Applied product growth frameworks (e.g., Reforge) to define funnel and retention metrics, building Tableau/Sigma dashboards to track MRR and feature adoption. Owned end-to-end event tracking in Google Analytics, delivering near real-time dashboards so Product Managers could act immediately on user behavior signals. Designed in-product, customer-facing analytics that bridged product and data strategy by defining the core metrics used to measure performance. Shaped executive roadmap priorities through deep-dive analyses of product usage, growth levers, and strategic business challenges.

Built a reporting framework from scratch using scheduled cron jobs, C#, Microsoft Excel, Google Sheets, and SQL, automating delivery of MRR, ARR, and cohort retention reports to leadership. Conducted A/B tests by splitting traffic across website variants to measure conversion rates, membership sales, and renewal rates, using results to optimize pricing models and contributing to a 30% increase in new customer acquisition. Performed root-cause analysis on revenue anomalies, distinguishing recurring vs. non-recurring revenue drivers to guide pricing and product decisions. Analyzed affiliate sales performance, Google Analytics keyword reports, and campaign ROI to optimize marketing spend. Designed and deployed data marts to improve data accessibility and reduce wait times by 75%.