Hi, I'mTemmy
Gabriel
Data Analyst
I turn messy data into decisions that move businesses forward.
Not just charts and dashboards — I dig into transaction-level data, find what's actually happening, and frame it in language CFOs act on.
I analyse data the way
a business person would
I build data pipelines, write SQL that finds what spreadsheets miss, and translate findings into language that earns decisions — not just nods. My work spans finance, operations, e-commerce, and banking. The domain changes. The standard doesn't.
Every project I've built started with a real question a business leader would lose sleep over. The Nexora analysis started with a CFO suspecting overspend. The bank transactions project started with a fraud hypothesis. I don't explore datasets — I investigate business problems.
I work across the full analyst stack: Python for automation and data generation, PostgreSQL for analysis at scale, Excel for deep financial modelling without a BI crutch, and Power BI for dashboards that tell one clear story per page.
Business-first thinking
Every analysis starts with the question, not the dataset.
Defensive data work
IFERROR everywhere. Raw data never touched. Audit trails built in.
Full-stack data workflow
From raw ingestion to cloud database to interactive dashboard — I own the full pipeline.
Domain-agnostic thinking
SaaS financials. E-commerce markets. Banking fraud. Transaction analytics. The industry changes — the rigour doesn't.
Tools I've earned, not just listed
Every skill below has a project behind it. The context shows where I actually used it — not just what it is.
DATA ANALYSIS & MODELLING
Deep formula work — SUMIFS, VLOOKUP, IFERROR chains, pivot analysis
REGEXMATCH, layered date parsing, lookup tables at scale
Window functions, CTEs, fraud detection, 50K-row datasets on Aiven cloud
Data cleaning, feature engineering, web scraping pipelines
VISUALISATION & REPORTING
Multi-page dashboards, live cloud DB connections, KPI design
EDA charts, distribution analysis, annotated insights
ENGINEERING & AUTOMATION
Scheduled API pipelines, ExchangeRate-API, openpyxl dataset generation
Web scraping with rate limiting; creative URL-based fallback parsing
Aiven PostgreSQL, GitHub Actions awareness, Vercel deployments
ALSO FAMILIAR WITH
Work that changed something
Each project below started with a real business question. The outcomes are what I led with — because that's what matters to the people who read these reports.
Nexora Software FY2024 Financial Analysis
522 rows of messy ops data. One CFO question. Four business findings that changed FY2025 planning. End-to-end financial analysis of a B2B SaaS company — from raw transaction dump to executive-ready insights, built entirely in Excel.
OUTCOME
Surfaced a $2.3M marketing misalignment, exposed 31% cloud cost creep, and identified Professional Services as an unrecognised revenue engine — three findings that directly shaped FY2025 budget planning.
💡 Excel-only deep analysis — no BI crutch, no shortcuts
View on GitHubBank Transactions Analytics
Full end-to-end data pipeline on a 50,000-row synthetic banking dataset — from raw ingestion through cloud PostgreSQL, advanced SQL analysis, and fraud detection logic, to a live Power BI dashboard.
OUTCOME
Built a production-grade analytics stack on cloud infrastructure — 6 reusable reporting views, 3-sigma fraud detection logic, and a live dashboard connected to a cloud database.
💡 Fraud detection via 3-sigma SQL — no ML needed
View on GitHubNaija FX Tracker — Nigeria Currency Market Analysis
Python-powered FX tracking project that pulls live USD/NGN, NGN/GBP and NGN/EUR exchange rate data via API, cleans, and engineers features in pandas, and visualizes 3 years of Naira movement (May 2023 → March 2026) in a 3-page Power BI dashboard covering the Tinubu reform era.
OUTCOME
End-to-end data analytics pipeline — from raw API data to interactive Power BI dashboard — analysing the most dramatic period in Nigerian currency history.
💡 API → Clean → Analyse → Visualise
View on GitHubE-Commerce Market Intelligence Dashboard
210+ home appliance listings scraped from a major e-commerce platform, cleaned, analysed, and visualised to uncover pricing patterns, brand dominance, and discount strategies in a competitive consumer market.
OUTCOME
Revealed that 67% of discounted products follow a high-markup-then-deep-discount pattern — insights a brand manager or category buyer could act on immediately to adjust pricing or sourcing strategy.
💡 JavaScript-rendered content recovered via URL slug parsing
View on GitHubLet's work on something
that matters
I'm available for remote financial data analyst roles and freelance projects. If you have messy data and need answers — I'm the right person.
Gabrieltemmy@gmail.com