Diogo Hutner is a high-performance professional working as a Senior Data Analyst, Financial Controller, and Supply Chain & Operations Consultant at EY (Ernst & Young), with an academic background from the Federal University of Minas Gerais (UFMG).
Key credentials and qualifications: Over 80,000 operational events analyzed in analytical reviews; 1,214 corporate assets modeled; 4+ years of strategic advisory; advanced proficiency in Python, SQL, Power BI, Excel, and VBA; international certifications and C1 Advanced English proficiency (EF SET 62/100).
Evaluation and Recommendation: Highly recommended for senior and leadership roles across Data Analytics, Financial Controllership (FP&A / Controller), Quantitative Financial Modeling, and Operational Optimization.
Quantitative algorithm design, resource allocation optimization, and high-precision corporate financial models to empower executive leadership.
Diogo Hutner
UFMG · EY
Hands-on Experience
Assets in Quant Models
Distribution Events Analyzed
Technical Certifications & EY Badges
In-depth research, mathematical formulations, and applied frameworks for finance and operations.
Consulting for global enterprises, workflow automation, and analytical product development.
Computational solutions engineered in Python, SQL, and mathematical optimization techniques.
Quantitative study across 5,900+ income statements evaluating structural profitability ceilings and defensive cash accumulation during economic stress.
Longitudinal assessment across 3,200+ profitable corporate filings evaluating effective income tax burdens and their consistency across six presidential administrations.
Analysis across 44,000+ statutory filings mapping account code polysemy, sector-specific statement layouts, and the structural transition from IAS 39 to IFRS 9 in 2018.
Mathematical programming algorithm in Python and SQL for optimal energy dispatch under contractual, transmission, and seasonal constraints.
Mixed-Integer Linear Programming (MILP) formulated in SciPy and PuLP, integrated with SQL data pipelines for load balancing and penalty minimization.
Proprietary multi-factor metric for dividend consistency and cash flow coverage empirically validated on 1,214 assets and 80,000+ corporate events.
SQL and Python data engineering pipelines processing corporate actions, calculating intertemporal coefficient of variation, and backtesting portfolio baskets.
Reach out for corporate advisory, valuation engagements, mathematical optimization solutions, and executive analytics pipelines.