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
✓ 7 models · 22 tests · 0 errors · 5,010 rows · green on DuckDB + SnowflakeSQL Data Warehouse on PostgreSQL
✓ 18 quality checks green · 60,398-row fact · revenue reconciled across layersInteractive Dashboard + Narrative
Customer Churn: Revenue at Risk
✓ 9 quality checks · 26.54% churn · $58,277/mo top segment riskExcel Analytics
Delivery SLA Impact on Customer Satisfaction
✓ 48.2pp satisfaction gap · p<0.001 · power=1.0 · holds across every region & distance quartileLLM Token Gateway + AI FinOps
✓ 42/42 tests green · 39/39 dbt (idempotent, 12,934 rows) · anomaly injected & caught (z=565)AWS Cloud Foundations
US Critical Care Lakehouse
Big Data Engines Benchmark
✓ DuckDB 22.73s · PyArrow 1,185s · Polars OOM-killed at 12.51 GiB (kernel log)Modular ETL Pipeline Engineering
✓ 4/4 tests green · 50 workbooks → 500 rows · verified from a clean cloneThe skill set behind the projects
A working subset of 25 skills pulled from a longer list, kept because each one shows up in a project above, not because it looks good on a resume.
Query & Programming
CTEs, window functions and API-to-warehouse scripting: AE-01, BA-01, DE-06, DA-01.
Modern Data Stack
Layered dbt models over a warehouse, plus the lakehouse and orchestration side: AE-01, DE-01, DE-02, DE-05, DE-06. Big Data engine trade-offs at scale: TRANS-04.
Cloud
S3, EC2, Lambda and least-privilege IAM behind the orchestrated and infra-as-code pipelines: AE-05, DE-01, DE-04, DE-05.
BI & Visualization
From a Power Pivot star schema in Excel to a cross-filterable web dashboard: BA-01, BA-02, DA-02, DA-03.
Statistics & Experimentation
Hypothesis testing, confidence intervals and power behind every A/B call: BA-04.
Business Analysis
Translating a business question into requirements, clean data and a recurring report: BA-01, BA-02, BA-03, DA-04.
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.
Delta Lake Internals: What Actually Happens When You Write, Update, and Delete
What the _delta_log is, why Parquet files are never modified in place, and what makes Time Travel and ACID guarantees possible.
Data Governance: The Eight Pillars Behind Trusted, Actionable Data
Most organizations don't have a data problem. They have a data trust problem. The framework that closes the gap between having data and being able to act on it.
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.