Luiz R.
Data Engineering
Luiz has worked in various industries, including retail, eCommerce, financial operations, sales, and real estate in Brazil.
His expertise includes text processing, sentiment analysis, and infrastructure design. Luiz has optimized ETLs, improved queries, and PySpark code, and implemented table partitioning. He has a proven track record of implementing innovative solutions, such as a StableDiffusion-based application for image enhancement and an XGBoost model for predicting buyer visit-scheduling probability.
Main expertise
Apache Spark4 years
Data Engineering4 years
Databricks1 year
ETL5 yearsExperience4
Data Engineer | Data Scientist
- Refined the team’s entire code structure, improving performance and code/ML reproducibility.
- Improved several ETLs by optimizing queries, PySpark code, and table partitioning.
- Worked with other engineering teams to map and define multiple improvement points for the entire Data Chapter.
- Researched and implemented a StableDiffusion-based application to "decorate" apartment photos.
- Created an XGBoost-based model to predict buyers’ visit-scheduling probability over time.
- Created and maintained multiple interactive data-viz prototypes using Streamlit and ML models for the Business Intelligence area.
- Defined and deployed multiple model APIs to production through a SageMaker layer alongside the MLOps team.
Data Science
Pandas
DataDog
Scikit-learn
Machine Learning
Data Engineer | Data Scientist
- Created text-processing PySpark jobs for multiple client pipelines on a lakehouse architecture.
- Designed and implemented a highly available infrastructure for a speech-to-text and text-processing engagement using GCP (Dataproc, R-MIG, Compute Engine, Firebase, Cloud Function, Build, and Run).
- Supported and developed machine learning models for sentiment analysis.
- Contributed to the creation of several PySpark jobs for multiple text-processing pipelines for different clients on a lakehouse architecture.
PandasScikit-learn
Data Engineer
- Automated data extraction procedures using a distributed architecture with Apache Airflow and Docker.
- Created Spark jobs for data indexing and logging of metadata, including relational database architecture and modeling for event logging.
- Automated procedures and ETLs using Bash, Python, and Docker.
- Developed database-interface APIs using Node.js.
Pandas
Data Scientist
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Implement a cluster using Kubernetes (GKE) to orchestrate dockerized ETLs in Python.
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Creation of a scalable development platform used for several engagements using Jupyter Hub hosted in a cluster with Kubernetes.
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Development of a workshop on data science, machine learning models, and their applications.
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Implement a supervised model to predict sales of new products based on historical data and visual characteristics, with direct development on the whole machine learning chain, from data processing to serving the model through a REST API using Flask.
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Establishment of a workflow to analyze and compare machine learning models using MLFlow.
Data SciencePandasScikit-learnMachine LearningEngineering excellence
All Data Engineerings who have applied to Proxify are scored from 0 to 300 on engineering excellence, one of the five parameters we evaluate. This score reflects engineering excellence only, based on interviews, take-home assignments, live coding sessions, and/or on-the-job performance reviews. The curve shows how all evaluated Data Engineerings are distributed across that range, where our acceptance threshold for this parameter sits, and where Luiz stands.
Score 212 · Top 4% of engineers
Education
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