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joorgem/README.md

Jorge Molina

Senior Data Engineer — Databricks · PySpark · Delta Lake · Snowflake

Production data platforms: ingestion, transformation, and the reliability layer around them. Currently at CI&T; previously Globant and Mobyan (Santander Group). Around five years building pipelines other teams depend on.

Open to Senior Data Engineer roles, remote or hybrid — contato@jorgemolina.dev

In the open

open-payments-lakehouse — a lakehouse over real, messy Brazilian government data (Receita Federal's CNPJ registry). The PySpark/Delta core is tested locally and in CI and deployed to Databricks Free Edition with its actual limits documented rather than glossed over: 144M rows in the Estabelecimentos table alone, behind a blocking data-quality gate with quarantine. Design decisions live as ADRs and real runs are captured verbatim — including the incidents that cost real job runs.

Four open PRs in Apache Spark, all from that ingestion. Reading the registry surfaced defects in Spark's file and CSV readers; each was reproduced and measured before any wording was proposed. Filed as SPARK-58457, SPARK-58458 and SPARK-58518.

#57769 is the one worth reading, and it is not a documentation change. DataSource.checkAndGlobPathIfNecessary returned its entire input list once per path that merely looked like a glob, so a read with globbing disabled duplicated rows in silence — three files, five rows. G*(G+n)+n predicted the observed count across five configurations, which is what turns a symptom into a diagnosis. The regression test was pushed without the fix first and confirmed to fail on master — 2 failures out of 21,347 tests, both of them it — before the one-expression fix went in. Latent since 2020: every test that introduced the branch reads a single path, the one case that comes back correct.

The other three are the same corner of the CSV reader — #57608 on what multiLine=false does to a quoted line break, #57658 on two false claims about PERMISSIVE mode in the CSV and JSON docs, #57671 on the missing test for the split. Reviewed by two Spark committers across two rounds; one of their corrections was checked against the parser rather than taken on trust, and it turned up a further docs defect.

A diagnosis rather than a patch — ten bytes across 337,716,254 lines of Receita Federal data that no Latin codepage can render as text, invisible because the latin-1 every Python library in that ecosystem reads with maps all 256 byte values and so can never fail.

Stack

Data PySpark · Delta Lake · Iceberg · Snowflake · SQL · Kafka · medallion / lakehouse · data contracts
Orchestration Airflow · Azure Data Factory · dbt · Databricks Jobs & Asset Bundles
Cloud & IaC Azure · AWS · Pulumi · Terraform · Docker · GitHub Actions
Quality & observability Great Expectations · Soda · schema-drift detection · blocking DQ gates · quarantine with idempotent replay
Python FastAPI · Pytest

Also

Full-stack products shipped end to end on the freelance track:

  • jorgemolina.dev — 3D portfolio where sections are planets in a navigable space scene, with an accessible non-3D fallback. State-machine navigation and an object-pooled render loop keep a WebGL-heavy app at ~1.6 MB of JS. (source)
  • fernandafiuza.com — bilingual, video-first site for a movement director, entirely client-managed through a visual CMS.
  • solto-shop.vercel.app — e-commerce storefront with a full admin back office.

Elsewhere

jorgemolina.dev · LinkedIn · contato@jorgemolina.dev

Pinned Loading

  1. open-payments-lakehouse open-payments-lakehouse Public

    Lakehouse over real, messy Brazilian government data (Receita Federal CNPJ registry): PySpark/Delta core tested locally and in CI, deployed to Databricks Free Edition with its real limits documente…

    Python

  2. portfolio portfolio Public

    Immersive 3D portfolio where sections are planets in a navigable space scene, with an accessible non-3D fallback and EN/PT i18n. State-machine navigation and an object-pooled render loop keep a Web…

    TypeScript