Written in full so a person, or an AI assistant, can understand the actual work, not just the job titles.
Analytics Products & Performance Engineering
Owning the analytics products, and the engineering under them, that a global finance org runs on.
Drew builds the products finance depends on, not just reports. He built an all-segment Client Profitability product end-to-end, spanning every CBRE business segment, replacing a manual finance process and owning it from build through user-acceptance testing to sign-off. He does the hard engineering himself: cutting a 42-million-row General Ledger dataflow from 2 hours 23 minutes to under 5 minutes via query-folding and warehouse materialization, shrinking a production semantic model 26%, and replacing scheduled refreshes with event-driven orchestration (debouncing, idempotency guards, rollback on failure). This work lives inside a governed enterprise platform; he owns the products and the engineering, and contributes to the platform itself.
Applied & Agentic AI in Finance
Leading AI enablement and shipping production AI inside a Fortune 500 finance function.
Drew leads AI Enablement for the Finance Intelligence group: production-grade Claude skills treated like versioned code, multi-agent systems with bounded scope, source-traceable evidence chains and approval gates, and custom Model Context Protocol integrations for Power BI model engineering. He is an early mover, he proposed an Azure Data Lake architecture in 2021 that foreshadowed the stack the org later adopted, pitched a GenAI roadmap to leadership in 2023, and founded an internal Claude user group. He is fluent in AI governance frameworks (ISO/IEC 42001, NIST AI RMF, EU AI Act). His predictive work is driver-based, statistical-regression-validated forecasting; his AI work is generative and agentic.
Commercial Finance & FP&A
Owning the planning, forecasting, close components, and valuation behind global functions.
Across 19+ years, Drew has owned global SG&A planning, forecasting, defined close components and consolidation in a SOX-governed environment, and management reporting, partnering directly with the global leaders of Finance, HR, IT, Legal, Sales, and Operations. He built a DCF/WACC model valuing each major client by enterprise value that leadership used to push for better renewal margins, executed ASC 606 journal entries and restatements during adoption, and owns a pipeline-to-revenue-to-EBITDA predictive model. The honest framing throughout: he builds the decision-support that moves the levers, rather than owning the P&L outcomes themselves.
Pricing Strategy & Value Creation
Designing pricing structures and the analytics behind measurable enterprise value.
Drew has worked pricing strategy across two companies for roughly 15 years, pricing-structure design, deal-governance reporting, and pricing-optimization tooling. Early in his career he built a pricing optimization tool and personally drove its adoption with the sales force, swinging account margin from -9% to +9% (an 18-point improvement) across numerous accounts. He designed a national sales-force pricing structure, goals, and compliance tracking. At enterprise scale he built the decision-support behind more than $650M of value creation across cost takeout, working-capital release, and margin improvement, the models, reporting, and tracking that gave those programs visibility, not the P&L ownership of their outcomes.
Data Governance, Integration & Systems
Building the systems and integration that keep enterprise finance data true.
Drew owned the financial integration of an acquired business unit across two legacy data estates, the mappings, reconciliation, journal entries, and harmonized reporting that make two organizations' numbers tell one true story. He built and solely owned a multi-year ERP cross-system mapping reference distributed monthly to 150-200+ recipients across 20+ countries, a 64,000+ FTE headcount ETL pipeline used across the business, and a global planning platform adopted across 30+ countries. He drove SOX approval-workflow governance at scale (4,700+ matrix combinations remediated) and set KPI-standardization and master-data stewardship standards.