Transforming Data into
Business Decisions

Where geopolitics, data & business strategy meet

Market and Geopolitical Intelligence Analyst with 6+ years bridging political risk, sector analysis, and data science across global trade, logistics, and heavy industry — energy, mining, and raw-materials extraction. An International Relations background combined with hands-on technical depth (SQL, DAX, Python, Power BI, Google BigQuery, AWS Glue, NoSQL) turns regulatory shifts, commodity cycles, and country-risk signals into corporate intelligence and due-diligence-grade analysis for decision-makers operating across borders. Track record includes leading an internal intelligence initiative that converted raw data into strategy, driving a 374% gross profit increase ($1.9M) and 198% revenue growth through automated dashboards and sector-risk reporting. Comfortable reading government tenders, auction calendars, and policy documents across commodity supply chains, and building the data infrastructure — cloud data warehousing, workflow automation, data storytelling — that makes that reading actionable for trading houses, majors, and risk-advisory teams alike.

• Data Strategy
• Data Architecture
• Business Intelligence
• Data Science
• Geopolitical Reading
• Data Modeling

Languages

EnglishFluent

MandarimIn Progress · HSK 1

HungarianBasic

SpanishProfessional

Bachalor's Dregree International Relations

UNIFESP - Escola Paulista de Política Economia e Negócios

Geopolitical & Country Risk · Political Risk Analysis · International Trade Policy · Regulatory & Compliance Analysis

Data Analysis Specialization

Alura

Python for Data Analysis (Pandas, Data Cleaning & Transformation) · SQL & SQLite (Joins, Views, Transactions) · MySQL (Procedures & Functions) · Power BI (DAX, Data Modeling, Interactive Dashboards) · Statistics with Python · Data Storytelling · Data Visualization · Excel (Advanced Functions & AI-Powered Automation)

International Business Specialization

ESRI - Escola Superior de Relações Interacionais

International Negotiation · Cross-Border Risk & Crisis Management · Market Entry Strategy (FDI, Joint Ventures) · International Business Consulting

Coorporate Diplomacy Specialization

ESRI - Escola Superior de Relações Interacionais

Corporate Diplomacy & Stakeholder Strategy · International Finance & FX Markets · Foreign Policy Analysis Applied to Business · Investment Attraction & Paradiplomacy

Tools I use to turn data into decisions

90-Day Sale & Market Intelligence Report

The business problem: An Energy solution supplier to national power transmission sector was tracking at 18.9% of its annual revenue target with no clear answer for why. Sales data lived in three disconnected places — a legacy spreadsheet, the CRM, and the inbound commercial mailbox — so leadership couldn't tell whether the gap was a demand problem, a conversion problem, or something happening after a deal was already quoted. This report pulls those three sources into one 90-day analysis to answer that question directly: only 2% of inbound e-mails ever convert to closed revenue, but the pipeline already open is more than large enough to close the gap — the real constraint is that 72% of lost deals fail for reasons inside the company's own control (pricing and delivery timelines, not client decisions), and 88% of closed revenue sits in a single client and two service lines. The report turns that diagnosis into an operating-model fix — named account ownership, a real-time CRM as single source of truth, cascading sub-targets, and a standing hand-off from Market Intelligence to Sales on which regulated auctions and sector investments to chase next — closing the loop between "here's what's wrong" and "here's what to do about it."

Tools

Tools used on this Study Case

Power BI

Pareto curve, cycle-time chart, loss breakdown, revenue consolidation, target tracking, deal table

CRM SYSTEM

Pipeline tracking, Win-rate calculation, Funnel data

🐍

Python

Chart Regeneration, Data Anonymization, Docx automation

Power Automate

Daily E-mail capture, Folder monitoring, CRM logging

Revenue Pace:

18.9% of annual target reached; current pace projects only 27% by year-end

Conversion Funnel:

2.3% of inbound e-mails convert to closed revenue (88% drop before reaching CRM)

Concentration Risk:

79.81% of revenue from one client; 88% from two service lines

Lost Revenue

$4.94M lost in 90 days — 72% from internal, controllable causes (pricing/delivery), not client decisions

Commercial Email Request Monitoring & Analysis

A pipeline to capture, clean, and analyze quotation requests received by email, built on Power Automate, Power Query, and Python.

• Continuous monitoring — automatic capture of new commercial requests, ready for bulk CRM entry.
• Historical reconstruction — full extraction of the mailbox to size request volume and feed funnel analysis.
• Reproducible ETL — turning raw data into an analytical base (client, thread, category) in both Power Query and Python.

An automated, always-on flow ("Monitorar cotações - Comercial") that captures new requests as they arrive.

The trigger points to a personal folder ("Commercial"), populated by an Outlook rule that automatically moves messages from the commercial list — avoiding the need for shared-mailbox access.

A manual, on-demand flow ("Histórico - Comercial") used to backfill the base with retroactive data, so analysis could start without waiting months for organic capture from Flow 1.

• Power Automate over Microsoft Graph API — the initial Python + MSAL + Graph API approach required Entra ID admin approval (Mail.Read), creating a blocking dependency. Power Automate sidestepped this because the Office 365 Outlook connectors already had pre-approved consent on the tenant.
• Keyword classification over AI (AI Builder) — AI Builder showed configuration instability and depended on Power Platform capacity credits; keyword filtering was kept for this phase.
• Grouping by ConversationId over subject text — normalizing subject text (stripping RE:/RES:/FW:) is fragile across language and formatting variations; the native ConversationId solves this deterministically.
• Manual over automatic pagination — "Get emails (V3)" exposes no native pagination above ~1,000 items; extraction was capped at 1,000 items (~99.8% of the mailbox) instead of building a manual pagination loop, prioritizing maintainability.

After ETL and thread grouping, the historical extraction identified dozens of distinct commercial requests, categorized by type and tied to the client's email domain — a structured base to answer volume, category, and client concentration on an ongoing basis, without manual effort.

• Semantic classification via AI Builder or a language model, replacing keyword filtering
• Automatic extraction of the requester's name and company from the email signature
• Migrating the analysis layer to Power BI, linked to the CRM, with average time-to-proposal calculated
• SQL-based ETL for more scalable consolidation and querying of requests
• A machine learning model to classify email intent, replacing the thread-based heuristic

# Same rule on both sides: "26-70", "26 - 070", "NBH-26-070" → "26-070"
PADRAO_REF = re.compile(r"(?<![\d:])(2\d)\s*[-–]\s*(\d{1,3})(?![\d:])")

def normalizar(texto):
    m = PADRAO_REF.search(str(texto))
    return f"{m.group(1)}-{int(m.group(2)):03d}" if m else None

Career timeline

Nari Brasil Holding

2026 - Market Intelligence Analyst

Deliver strategic market intelligence helps the organization navigate competitive landscapes and consumer behavior through in-depth data analysis.

Bridge the gap between raw data and business strategy by integrating data management, data architecture, and advanced analytics with marketing insights.

Perform market research, develop competitive intelligence, and build Power BI performance dashboards to support executive decision-making.

Manage data assets, architect scalable data solutions, and apply data science techniques to support marketing operations.

Convert market uncertainty into strategic clarity by building a solid, scalable, data-driven foundation for organizational planning.

COLI Shipping & Transport do Brasil

2023 - 2026: Sales Executive & Market Intelligence Analyst

Orchestrated data strategies that drove a 374% gross profit increase ($1.9M) and 198% revenue growth by developing automated Power BI dashboards for real-time KPI monitoring.

Streamlined executive decision-making through monthly data storytelling reports, aligning international pricing strategy with macroeconomic trends and cargo demand.

Led strategic commercial operations for global heavy machinery manufacturers and trading firms, specializing in the China–Brazil trade lane and high-value project cargo cycles.

Designed and led a proprietary market intelligence project applying International Relations methodologies and investment analysis to track global trade flows, using Pipedrive CRM and Skychart to convert raw data into high-value sales leads.

Managed end-to-end CRM-based project tracking to monitor team productivity, account adherence, and feasibility, ensuring commercial efforts stayed aligned with strategic goals and operational capacity.

Get in contact

Always glad to talk shop, sector trends, data pipelines, or where geopolitics and commodities intersect. If something here resonates with a role you're hiring for, or you just want to compare notes, reach out.