Staff Data Platform Engineer
I build and operate Databricks and AWS data platforms, from ingestion and streaming to analytics, governance, reliability, and cost control.
01 / Selected work
Two stories of architecture, implementation and the decisions that shaped production systems.
End-to-end technical ownership of a Databricks platform for analytics and production ML, including reliability, governance, infrastructure and cost.
Batch and streaming engineering, Terraform-managed infrastructure, and a controlled migration from on-premises systems to AWS in a hybrid financial-data estate.
02 / Production outcomes
Selected results from platforms built and operated in production.
Led the Databricks platform from initial architecture through implementation and production operation, covering ingestion, analytics, governance, data quality, observability, and model delivery.
Read the case studyDeveloped and improved Snowflake and AWS batch and streaming pipelines with PySpark, Kafka, and dbt, removing ingestion and processing bottlenecks.
Read the case studyMigrated 40% of high-volume financial-operations data to AWS, using AWS DMS for part of Fiserv's card transaction data. Separately optimized workloads retained on premises under PCI DSS constraints.
Read the case studyBuilt Snowflake and PySpark data products and Kafka streaming pipelines for payments, reconciliation, executive analytics, and regulatory reporting.
03 / Experience
From banking data products and cloud migrations to building a Databricks platform from scratch, my work connects architecture, implementation and sustained operation.
Focused professional development in data-platform architecture, cloud and hybrid systems, Spark processing, and batch and streaming design. Deepened practical knowledge of orchestration, data quality, observability, governance, infrastructure as code, CI/CD, and the data foundations for analytics and ML.
Built ETL and analytical workflows to identify and correct customer-data inconsistencies in banking. Analyzed Snowflake query plans and ingestion patterns, identifying bottlenecks and providing performance recommendations to engineering and business stakeholders.
Supported data preparation, ETL development, and workflow automation for banking analytics and data-engineering delivery. Worked with senior engineers on Snowflake performance analysis and reliable analytical datasets, building a foundation in Python, data quality, and production workflows.
04 / Capabilities
Hands-on engineering across ingestion, processing, storage and consumers, with infrastructure, reliability, governance and cost managed throughout.
05 / Operating principles
Architecture decisions are evaluated through production behavior: reliability, change safety, team autonomy, and lifecycle cost.
Define ownership, SLOs, recovery, cost controls, and change management alongside the platform architecture.
Improve performance across ingestion, processing, storage, and consumers while managing reliability and lifecycle cost.
Standards, automation, and self-service should make the correct implementation the easiest one to repeat.
Education
B.Sc. in Computer Engineering — Universidade Presbiteriana Mackenzie, 2021
Electronics Technician — SENAI São Paulo, 2015
Selected certifications
Data Architect Nanodegree — Udacity
Data Product Manager Nanodegree — Udacity
Data Streaming Nanodegree — Udacity
Data Engineering with AWS Nanodegree — Udacity
Machine Learning DevOps Engineer Nanodegree — Udacity
Machine Learning Specialization — DeepLearning.AI
Machine Learning Engineering for Production (MLOps) — DeepLearning.AI
Contact
I’m interested in Staff and Lead Data Platform Engineering roles involving Databricks, AWS, platform architecture, and hands-on production ownership. Share the team, platform challenge and scope to start a conversation.