Brunno Araujo De Albuquerque

I’m a Senior Data Engineer with 9+ years of experience building and evolving cloud data platforms across Azure and GCP, with strong expertise in ETL/ELT, complex data migrations, and scalable architectures for enterprise analytics. I specialize in Databricks and Apache Spark (PySpark, Structured Streaming, Delta Lake) and in implementing Medallion Architecture (Bronze/Silver/Gold) with a strong focus on data quality, schema enforcement, observability, and governance (naming standards, documentation, and traceability).

Over the years, I have worked on migrations from legacy and on-premises systems to cloud environments, defining incremental/CDC strategies, reconciliation processes, and high-precision validation to ensure transactional consistency. I have hands-on experience with BigQuery for analytical modeling and query optimization, including transforming complex nested JSON datasets (e.g., marketing and event data) into relational and analytics-ready models. I’ve also contributed to modernization initiatives (monolith to microservices), modeling domain entities and events (order, product, catalog, freight, etc.) from JSON payloads and producing data dictionaries, primary/foreign keys, and table relationships to enable reliable BI and analytics consumption.

I’m experienced in orchestration and automation using Airflow, as well as Azure-native tooling (Fabric, Synapse, ADF, Azure SQL). I’ve supported production environments and performed performance tuning end-to-end—from Spark job optimization and skew mitigation to cost optimization and reliability improvements. I’m a hands-on, quality-first engineer with strong stakeholder communication skills, able to translate business and product requirements into resilient, maintainable data pipelines. Advanced English (C1).