Data Engineering
Scalable lakehouse, ETL/ELT and distributed data platforms.
AI • DATA • CLOUD
RENANDE LIMA ANDRADE
Principal AI & Data Engineer
Building scalable data platforms, cloud architectures and intelligent AI systems for real-world problems.
LLMs•AI Agents•RAG•Databricks•Spark•Multi-Cloud
01About
Engineering data.Building intelligence.
I design and build scalable data platforms and AI systems, connecting data engineering, cloud architecture and generative AI to transform complex technical challenges into reliable, production-ready solutions.
My work spans modern lakehouse architectures, distributed data processing, LLM applications, RAG, AI agents, MLOps, observability and multi-cloud environments.
12+Years in Technology
4Cloud Ecosystems
AIGenerative AI & Agents
DATAPlatforms at Scale
AI Engineering
LLMs, RAG, AI Agents and intelligent production systems.
Cloud Architecture
Multi-cloud architectures across AWS, Azure, GCP and OCI.
MLOps & Platforms
Observability, governance, deployment and AI lifecycle.
Databricks•Apache Spark•Python•Airflow•DBT•LLMs•RAG•AI Agents
02Experience
From voice & data foundationsto intelligent systems.
A technology career evolving through software, Business Intelligence, Big Data, cloud data engineering and Machine Learning into scalable Data & AI platforms.
MAR 2026 — OCT 2026
Nexus Tech
Data & AI Engineer
Designed and implemented an AWS Data Lakehouse consolidating transactional PostgreSQL data, CRM systems, application logs and partner files into a governed analytical platform, eliminating direct analytical queries against the production database.
CDC ingestion with AWS DMS and batch pipelines with AWS Glue
Bronze, Silver & Gold architecture with Databricks and Delta Lake
Dimensional modeling for transactions, customers and products
Data quality, governance, observability and incident runbooks
Impact
Reports that previously took hours were reduced to minutes, while direct analytical queries against the production database were eliminated and business metrics became consistent across teams.
AWSAWS DMSAWS GlueAmazon S3DatabricksDelta LakeAirflowGreat ExpectationsAthenaPower BIUnity CatalogPrometheusGrafanaPagerDuty
JAN 2025 — FEB 2026
IBM
Senior AI & Data Engineer
Modernized the data architecture of a digital retail company, building a scalable Data Warehouse and preparing the platform for Machine Learning initiatives across ERP, CRM, e-commerce and payment data.
Medallion architecture with distributed Spark processing
Kimball dimensional modeling and SCD Type II
Feature engineering and ML lifecycle integration with MLflow
AI agents and workflow automation with n8n
Impact
Reduced report generation time by 65% and direct queries against the transactional database by 40%.
SparkPythonAirflowdbtMLflowStreamlitn8nData WarehouseKimballSCD Type II
DEC 2022 — JAN 2025
Nexus Tech IT
Data Engineer / Consultant
Designed and implemented an AWS Data Lakehouse for financial data processing, focused on scalability, data quality, security, governance and regulatory compliance.
Medallion architecture on Amazon S3 with distributed Spark processing
ETL and metadata cataloging with AWS Glue and Athena
Airflow orchestration with SLA monitoring and failure handling
Security, governance and LGPD-oriented access controls
Impact
Established a governed and observable analytical foundation for financial workloads, integrating engineering and ML lifecycle practices.
AWSAmazon S3Amazon EMRApache SparkAWS GlueAthenaAirflowLambdaEventBridgeCloudWatchLake FormationIAMAWS KMSMLflow
JUL 2021 — NOV 2022
Global Hitss · Claro
Senior Data Engineer
Improved Claro's data architecture supporting marketing campaigns in a hybrid environment with batch and streaming workloads, while developing NLP capabilities for customer satisfaction analysis.
Batch and streaming data engineering with Spark
Integration with Amazon Kinesis and S3
NLP for customer satisfaction analysis with NLTK and spaCy
Migration toward serverless architectures for cost reduction
Impact
Combined distributed data processing, NLP and cloud modernization to support marketing analytics and customer intelligence.
AWSSparkKinesisAmazon S3PythonNLTKspaCyMLflowAWS GlueDataBrewStreamlit
MAY 2019 — JUL 2021
Magna Sistemas · São Paulo State Department of Education
Data Engineer
Developed a Machine Learning solution for predicting school dropout, integrating legacy sources, data collection processes and analytical Data Marts in an Azure-based environment.
Machine Learning model for school dropout prediction
Legacy data integration, data collection and Data Marts
Feature engineering and experiment tracking with MLflow
Interactive educational analytics with Power BI and Streamlit
Impact
Delivered analytical tools and educational indicators used to support executive-level decision-making and meetings with the State Secretary of Education.
AzureAzure Data FactoryAzure SynapseSQL ServerSparkPythonMLflowPower BIStreamlit
FEB 2016 — FEB 2019
Data Self
Mid-Level Data Engineer
Professional experience in data engineering, contributing to the evolution of a career increasingly focused on data platforms, analytics and scalable processing.
MAY 2014 — APR 2016
CSU Cardsystem
BI / Big Data Analyst
Worked with Business Intelligence and Big Data during the transition from traditional analytical environments toward modern data engineering.
MAY 2011 — JUN 2013
Telemidia & Technology International
Voice Recognition Developer
Early professional experience in voice recognition development, establishing the software and AI foundations that would later converge with data engineering and intelligent systems.
03Projects
Selected systems.
Built beyond the demo.
Engineering projects spanning intelligent agents, enterprise data platforms, MLOps and technology-driven innovation.
01 • FEATUREDAI Engineering
Enterprise AI
Agent Platform
Enterprise platform for building, orchestrating and monitoring intelligent AI agents, with multi-agent collaboration, semantic search, RAG, contextual memory, API integrations and auditable execution.
PythonFastAPILangChainCrewAILlamaIndexRAGVector DBOllama
View repositoryPublic repository
Orchestrator
Agents
RAG
Memory
APIs
Multi-Agent Architecture
02
DATA ENGINEERING
Modern Enterprise Data Lakehouse
Enterprise-grade blueprint for scalable Data Lakehouse and Data Mesh architectures, combining distributed PySpark pipelines, Medallion Architecture, dbt quality gates, Terraform and governance patterns.
PySparkData LakehouseData MeshdbtTerraformMedallionSCD Type IIIAM
View repository03
MLOPS
MLOps End-to-End Pipeline
End-to-end MLOps architecture automating the machine learning lifecycle from data ingestion and feature engineering to experiment tracking, deployment-oriented workflows, validation and production monitoring foundations.
PythonAirflowPrefectMLflowDockerPySparkGreat ExpectationsPydantic
View repositoryMore engineering work on GitHub
New projects, experiments and architecture blueprints.
04Expertise
Technology is the tool.Engineering is the discipline.
A technology stack built around scalable data systems, production AI, cloud platforms and engineering automation.
01
AI Engineering
Production-oriented intelligent systems combining language models, retrieval, tools and autonomous workflows.
Large Language Models
RAG
AI Agents
MCP
Embeddings
Vector Search
Prompt Engineering
Semantic Retrieval
02
Data Engineering
Distributed platforms and pipelines designed for reliability, scale, governance and analytical workloads.
Apache Spark
PySpark
Databricks
Apache Airflow
dbt
Delta Lake
ETL / ELT
Lakehouse
03
Cloud & Platform
Cloud-native engineering across multiple ecosystems, from infrastructure to scalable data services.
AWS
Microsoft Azure
Google Cloud
Oracle Cloud
Docker
Kubernetes
Terraform
Linux
04
MLOps & Architecture
Engineering practices for operating data and AI systems reliably throughout their production lifecycle.
MLflow
Observability
CI / CD
Data Quality
Governance
FinOps
System Design
Architecture
Engineering Stack
Data • AI • CloudTools I build with.
PythonSQLSparkDatabricksAirflowdbtMLflowDockerKubernetesTerraformPostgreSQLMongoDBRedisGitGitHubREST APIs
Architecture first
Designing systems around constraints, scale and evolution.
Automate everything
Reducing operational friction through engineering.
Production mindset
Building beyond notebooks, prototypes and isolated demos.
05Contact
Have a complex problem?
LET'S BUILD
SOMETHING
INTELLIGENT.
Data Platforms•AI Systems•Cloud Architecture•Engineering
Direct contact