README.md

Hi, I'm João Blasques 👋

I'm an AI-Enabled Data Engineer with over 7 years of experience designing, building and optimizing data pipelines and machine-learning solutions. I turn complex data challenges into actionable insights and automated systems that drive real business value.

Core expertise

  • Data Engineering — ETL/ELT pipelines, data warehousing, stream processing
  • AI & Machine Learning — TensorFlow, PyTorch, scikit-learn, NLP
  • Cloud Platforms — AWS, Google Cloud Platform, Azure
  • Big Data — Spark, Databricks, Snowflake, Kafka, Airflow
  • DevOps / MLOps — CI/CD, testing, Terraform (IaC), Docker, Kubernetes

Curious about my work? Browse my posts and projects, or read more about me.

559 contributions in the last year

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vitals Site

From raw clinical signals to trusted, AI-ready data — a health-data medallion lakehouse serving analytics marts, an ML feature store and a vector index (Databricks/Delta, dbt, Airflow, Feast, pgvector).

Python
corpus Site

A knowledge base that tends itself. Sources in, cited pages out — an LLM agent reads everything, writes it into a cross-linked web of citable pages, and keeps it consistent as new sources arrive.

Python
nora Site

An email-native AI executive assistant. Nora drafts, the human sends — that asymmetry is the product's trust model.

Python

A self-managing MBTA on-time-performance lakehouse on Databricks + GCP, with an agentic layer that writes its own insights and opens pull requests.

Python

Daily ELT pipeline that identifies high-value customers for sales outreach — Airflow, dbt, Spark, Iceberg, Docker.

Python

Real-time e-commerce analytics platform using Apache Spark, PySpark and Kafka.

Python

Popular posts

A live transit-data lakehouse that ingests MBTA real-time feeds, computes on-time performance, and runs, heals, and improves itself — with an agentic layer that writes its own insights and opens pull requests.

data engineeringdatabricksgcplakehouse

A production-grade ELT pipeline that automates daily identification of high-value customers using Apache Airflow, dbt-spark, and Apache Iceberg.

data engineeringairflowdbtspark

A comprehensive guide to implementing modern analytics engineering practices using dbt, BigQuery, and Looker Studio. Learn to build scalable data transformation pipelines with dimensional modeling, testing, and deployment strategies.

analytics engineeringdbtbigquerydata transformation

A comprehensive guide to implementing a scalable, cost-effective data pipeline and warehouse using Google BigQuery, featuring external tables, partitioning, clustering, and performance optimization with NYC taxi data.

data engineeringbigquerydata warehousecloud