Tomás Lucena

CEO

Recife, Pernambuco, Brazil6 yrs 5 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in modernizing data architectures and pipelines.
Proven track record in real-time data processing.
Recognized for creativity and proactive contributions.

Stackforce AI infers this person is a Data Engineering expert in Healthcare and Financial sectors.

Contact

tomas.lucena@outlook.com LinkedIn

Skills

Core Skills

Data ArchitectureAi ImplementationAutomationData ProcessingData EngineeringReal-time Data ProcessingData ScienceData Monitoring

Other Skills

llmGoogle Kubernetes Engine (GKE)BI SolutionsApache SparkApache KafkaRedisInfluxDBPythonAWS GlueAmazon RedshiftArquitetura de dadosEngenharia de dadosAmazon Web ServicesETL (Extração, transformação e carregamento)PostgreSQL

About

Data Engineer with 6 years of experience in modernizing data architectures, developing high-performance pipelines, and optimizing storage and analytics solutions using tools like AWS Glue, Spark, Redshift, S3, and Azure DevOps. Expertise in real-time data processing with Kafka, Redis, and Python, alongside building dashboards and automating processes. Proven ability to design scalable systems, manage migrations, and drive efficiency in financial and investment contexts, with consistent recognition for critical thinking, creativity, and proactive contributions.

Experience

6 yrs 5 mos

Total Experience

1 yr 6 mos

Average Tenure

1 yr 1 mo

Current Experience

Darede

Data Tech Lead

May 2025 – Present · 1 yr 1 mo · São Paulo, Brazil · Remote

Bren

Data Architect

Feb 2025 – Feb 2026 · 1 yr · São Paulo, Brasil

Leading end-to-end development at an AI company building LLM-powered sales agents.
Driving architecture and implementation across all systems, with a focus on performance, scalability, and innovation in applied AI.

llmGoogle Kubernetes Engine (GKE)Data ArchitectureAI Implementation

Fix.it br

Data Architect

Sep 2024 – Mar 2025 · 6 mos

Development of modern and scalable data architectures for day clinics and hospitals, ensuring greater
efficiency and accessibility of information.
Modernization of management and operational reports, replacing manual processes with automated, BI-based solutions.
Creation of intelligent pipelines for competitor monitoring, leveraging public data to provide strategic insights and support decision-making.
Implementation document processing, enabling the reading and interpretation of previously unusable files, extracting valuable information for hospital analysis and management.
Development of predictive models for hospital demand forecasting, integrating internal and public data to reduce medical idleness and optimize workforce allocation.

Artesanal investimentos

2 roles

Data Architect

Jun 2024 – Sep 2024 · 3 mos · São Paulo, Brasil

Leadership in modernizing the data architecture of Artesanal’s credit funds.
Developed high-volume data processing pipelines using Glue Jobs + Spark.
Designed optimized data cubes in Redshift, accelerating queries and analysis in BI tools.
Implemented efficient data ingestion routines in Redshift utilizing AWS Lambda + Glue Jobs.
Structured data lakes on S3, reducing storage costs and improving data access efficiency.
Conducted migrations of legacy databases to Amazon Redshift.
Managed cloud resources and built CI/CD pipelines, automating deployments with Azure DevOps.

Data ArchitectureApache SparkData Processing

Data Engineer

Mar 2022 – Jun 2024 · 2 yrs 3 mos · São Paulo, Brasil

Leadership in modernizing investment strategies for Artesanal’s multimarket fund.
Developed investment strategies using Python.
Created real-time dashboards with low latency leveraging Kafka + Redis + Python
Built ETL pipelines using Lambda + PostgreSQL.
Recognized for creativity and proactivity in automating routine tasks

Apache KafkaRedisData EngineeringReal-time Data Processing

Afal

Data Scientist

Jan 2020 – Mar 2022 · 2 yrs 2 mos · Remote

Developed systems for validation and monitoring of investment strategies in financial markets.
Created interactive dashboards using Grafana and Dash.
Built robust pipelines for real-time and historical data processing.
Utilized time-series databases (InfluxDB) for efficient and scalable data management.
Praised for proactivity and contributions to the team through deliverables and knowledge sharing.

InfluxDBPythonData ScienceData Monitoring