Turning complex datainto decisionsteams can trust.
Engineering student and analytics builder focused on SQL, Python, and Power BI, creating decision-ready dashboards, KPI systems, and BI workflows with a business-first, internship-ready mindset.
Decision intelligence architecture
FROM hiring_ready
WHERE focus = 'analytics';
Engineering analytical thinking, end to end.
I'm an engineering student building a career around data analytics, business intelligence, and financial analytics. My work centers on translating raw, messy data into clear, decision-grade insights — through structured problem solving, thoughtful dashboard design, and disciplined storytelling.
I care about the why behind the numbers: how a cohort retention curve reveals product fit, how RFM segments reshape acquisition strategy, how vintage analysis exposes portfolio risk before it surfaces in headline KPIs.
Self-taught across SQL, Python, Pandas, Tableau, and Power BI — with a working interest in financial analytics and risk intelligence systems used by modern fintechs and institutional desks.
The toolkit.
Categorized by function — the way analytics teams actually scope work.
Data Analytics
Business Intelligence
Financial Analytics
Programming
Visualization
Databases
Tools
Projects shipped like case studies.
Each project frames a business problem, the analytical approach, and the insights that surfaced.
Olist Sales Analysis
Decode revenue drivers and customer behavior across a multi-category Brazilian marketplace.
- ▸Category-level revenue concentration
- ▸Order-volume seasonality
- ▸Geographic demand mapping
E-Commerce Cohort Retention Analysis
Quantify how user cohorts retain over time and where the lifecycle leaks.
- ▸Month-over-month retention curves
- ▸Cohort heatmap matrices
- ▸Lifecycle drop-off windows
RFM Customer Segmentation
Rank and segment customers by Recency, Frequency, and Monetary value to inform retention strategy.
- ▸5 actionable segments
- ▸Champions vs At-Risk mapping
- ▸CLV-aligned targeting
Daily Reflection Tree
Build a structured self-reflection system that turns daily inputs into compounding insight.
- ▸Hierarchical journaling model
- ▸Pattern surfacing across entries
- ▸Habit-tracking primitives
Financial Analytics & Risk Intelligence
Generic competencies modeled on institutional analytics platforms — portfolio monitoring, risk surveillance, and BI workflows used by modern fintechs.
Portfolio Monitoring
Tracking exposures, allocations, and performance across portfolios with structured KPIs.
Delinquency Analysis
Bucket-level delinquency tracking, roll-rate modeling, and early-warning trend detection.
KPI Systems
Designing institution-grade KPI hierarchies that align operations to strategy.
Vintage Tracking
Cohort-style vintage curves to evaluate origination quality across periods.
Stress Testing Concepts
Scenario modeling and sensitivity analysis to quantify downside exposure.
Investor-Style Reporting
Clean, defensible reports modeled on institutional investor packs.
Visuals from the workbench.
A gallery of analytics primitives built across the projects.
How I think about analytics.
Business-First Analysis
Start from the decision, not the dataset. Every chart serves a question.
KPI-Driven Decision Making
Anchor work to measurable outcomes — define them before you build.
Structured Problem Solving
Decompose problems into components, test assumptions, iterate quickly.
Dashboard Usability
Design for the reader: hierarchy, density, and clarity over decoration.
Data Storytelling
Numbers earn meaning through narrative. Lead with the takeaway.
Analytical Discipline
Reproducible code, defensible methods, documented assumptions.
Risk-Focused Thinking
Pay attention to tails — what breaks, what leaks, what compounds.
Coding consistency.
A snapshot of contributions and analytics-focused repositories.
From syntax to systems.
Python Basics
Foundations of programming and computational thinking.
SQL
Querying, joins, window functions, and data modeling.
Data Cleaning
Handling messy data — nulls, duplicates, schema drift.
EDA
Exploratory analysis with Pandas, NumPy, Seaborn.
Dashboarding
Tableau, Power BI — building decision-grade dashboards.
Financial Analytics
Risk, KPIs, vintage and portfolio analysis.
Analytics Engineering
Building systems that scale insight delivery.
Let's build something measurable.
Open to internship and analytics roles. The fastest way to reach me is below.