André Matiello
I turn raw data into models a business can trust and act on.
A modern data portfolio built on real market data — SQL, dbt, Python and BI, with a bit of AI used with judgment, not as a crutch.
$ dbt build --project-dir ae-01 00:16 1 of 7 OK created sql view stg_market__prices 00:16 3 of 7 OK created sql view int_daily_prices_enriched 00:16 7 of 7 OK created sql table fct_daily_prices Completed successfully Done. PASS=29 WARN=0 ERROR=0 ✓ 7 models · 22 tests · 0 errors · 5,010 rows
Built on real market data
Data Analyst at the core, with range across Analytics & Data Engineering. Click any card for the full write-up — context, architecture, challenges and results.
Modern Data Stack ELT
Declarative ingestion with dlt, layered dbt transformation and a dimensional model — DuckDB in dev, portable to Snowflake.
✓ 7 models · 22 tests · 0 errors · 5,010 rows · green on DuckDB + SnowflakeInteractive Dashboard + Narrative
Multi-page BI dashboard with cross-filters and a 2-minute narrated demo on real market data.
Customer Churn — Revenue at Risk
Segmentation and revenue-at-risk quantified with SQL window functions and a business-facing dashboard.
Excel Analytics
Excel as a real analytics engine: Power Query ETL, a Power Pivot star schema, DAX measures and what-if scenarios.
A/B Test Analysis
Experimentation done right: hypothesis test, p-value, confidence interval and power analysis behind a clear call.
LLM Token Gateway + AI FinOps
A FastAPI proxy over LLM APIs feeding a medallion lakehouse that answers: which team is burning the AI budget?
The curriculum behind the projects
The foundations I'm building on — mapped to what the US market actually asks for.
SQL for Data Analytics
Querying, joins, CTEs and window functions — the #1 skill in data analyst postings.
Python for Data Analytics
pandas, APIs and automation for real analytical work.
dbt & the Modern Data Stack
Testing, dimensional modeling and CI for analytics engineering.
Statistics for Experimentation
Hypothesis testing, confidence intervals and power — the basis for trustworthy A/B analysis.
Notes from building
The decisions behind the projects — and what the data says about this market.
Lessons from Building a Modern Data Stack End-to-End
Grain, idempotency, warehouse portability — and why PASS=29 is not a test count.
What the Data Job Market Actually Asks For
Reading 262,000 real job postings instead of opinions — and where to start.
The Analytics Engineer's Toolkit
What each tool in the modern data stack is for — and when you don't need it.
How an Analytics Engineer Works: Method Over Tools
From the business question to a green build — the method that survives a tool migration.