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Victor Eduardo Pato Paulillo
Verified Expert in Engineering
Data Engineer and Developer
São Paulo - State of São Paulo, Brazil
Toptal member since March 8, 2021
Bio
Victor is a data engineer with nine years of experience in fintech, edtech, and SaaS industries. He's built data warehouses, pipelines, integrations, frameworks, modeling, and analytics solutions, as well as applied AI features such as RAG flows and LLM-powered capabilities. Victor is skilled in Python and SQL, with strong experience in cloud data platforms, lakehouse architectures, and deploying AI to production.
Portfolio
Chegg - Toptal
Redshift, Databricks, SQL, Python, APIs, Pandas, Data Pipelines, Data Modeling...
AqueleApe - Self-employed
Supabase, Vibe Coding, Data Engineering, Data Scraping, OpenAI, Python, SQL...
StoneCo
Apache Airflow, Apache Kafka, Hadoop, Apache Hive, ETL, Python...
Experience
- SQL - 8 years
- ETL - 7 years
- Data Engineering - 7 years
- Python - 6 years
- Amazon Web Services (AWS) - 6 years
- Apache Airflow - 5 years
- Databricks - 4 years
- Large Language Models (LLMs) - 2 years
Preferred Environment
Apache Airflow, Google Cloud Platform (GCP), SQL, Python, ETL, Databricks, Amazon Web Services (AWS), Tableau, Large Language Models (LLMs), Artificial Intelligence (AI)
The most amazing...
...thing I created was a data environment with data quality, Git version control, and CI/CD tests for 250+ internal and external tables and 30+ transformed tables.
Work Experience
Data Engineer (via Toptal)
2021 - PRESENT
Chegg - Toptal
- Profiled and modeled data in a data warehouse for the client by analyzing 30 existing reports and 120 tables.
- Utilized Databricks to develop multiple Python jobs to extract data from vendor APIs and load it into Redshift tables. Implemented comprehensive data quality checks and process controls to ensure accuracy and reliability throughout the pipeline.
- Built, modeled, and maintained a data pipeline on Databricks to load data into Redshift and Tableau Server. Created a data mart structure with 30 tables that support the data analytics and data science team with more than 100 Tableau reports.
- Designed and implemented a robust data quality framework with a Tableau reporting system to identify and promptly alert on any data anomalies or discrepancies.
- Migrated the data structure of ten tables on Looker to Tableau Server by building the data pipeline for marketing tables.
- Engineered a data framework in Python for seamless loading of external data into Redshift, adhering to SCD Type 2 standards for comprehensive historical tracking and analysis.
- Developed a robust CI/CD testing framework on GitLab, ensuring the quality and accuracy of project updates. Conducted comprehensive checks to detect any overlooked credentials, eliminate hardcoding of database parameters, and rectify query errors.
- Migrated data platform from AWS Redshift to Databricks Lakehouse using medallion architecture (bronze, silver, gold) and software engineering principles to build modular, testable pipelines, resulting in a unified platform and better maintainability.
- Created an AI-powered Student Page in Retool and built the full data pipeline, handling ingestion and transformation. The tool provides consistent insights into student behavior and curriculum progress, cutting research time from 20 minutes to 5.
- Implemented a streaming pipeline using Databricks Auto Loader to ingest email data in near real time, applying transformations and delivering a ready-to-consume Lakehouse table for real-time reporting.
Technologies: Redshift, Databricks, SQL, Python, APIs, Pandas, Data Pipelines, Data Modeling, Data Warehousing, Data Marts, Tableau, Dashboards, Amazon Web Services (AWS), Apache Airflow, GitLab, Data Quality, GitLab CI/CD, Databases, PySpark, Retool, Amazon Redshift, Microsoft Excel, Data Governance, Data Scraping, Web Scraping, Large Language Models (LLMs), Website Data Scraping, API Integration, Data Security, Delta Live Tables (DLT), Reporting, Data Lakehouse, Artificial Intelligence (AI), Machine Learning, RAG Pipelines, Retrieval-augmented Generation (RAG), OpenAI, Salesforce, Relational Databases, Star Schema, CI/CD Pipelines, Data Orchestration, Performance Optimization, ETL Pipelines, Database Schema Design, Business Analysis, Product Lifecycle Management (PLM), Spark, Data Integration, Batch
Founder and Data/AI Engineer
2025 - 2025
AqueleApe - Self-employed
- Built an end-to-end real estate SaaS for São Paulo home seekers, developing all back end and data pipelines using Supabase, custom scrapers, and automated ingestion flows for price history, safety data, and market signals.
- Designed and implemented advanced property search features—including intelligent filters, image-based property evaluation using AI, and standardized data models—enabling users to compare whether listings were overpriced or below market.
- Cleaned, transformed, and structured large volumes of external real estate and public-safety datasets into production-ready Supabase tables supporting fast queries and a consistent user experience.
- Built the entire application UI using Lovable (vibe coding) while managing product design, analytics, and iterative improvements based on user behavior and feedback.
- Launched and validated the product through TikTok marketing, reaching 50k+ views on a top video, onboarding 200+ free users, and converting 10 paying customers before concluding the project.
Technologies: Supabase, Vibe Coding, Data Engineering, Data Scraping, OpenAI, Python, SQL, ETL, Data Cleaning, Row-level Security (RLS), PostgreSQL, Large Language Models (LLMs), UX Design, B2C Marketing, Relational Databases, Data Orchestration, Performance Optimization, ETL Pipelines, Database Schema Design, Business Analysis, Batch
Data Engineer
2021 - 2021
StoneCo
- Created batch and streaming data pipelines for business teams.
- Loaded data from external data providers on the Data Lake.
- Created a data quality system for data processing jobs.
Technologies: Apache Airflow, Apache Kafka, Hadoop, Apache Hive, ETL, Python, Amazon S3 (AWS S3), Redshift, SQL, ELT, Data Quality, Data Processing, Jupyter Notebook, Pandas, NumPy, Data Engineering, Data Modeling, Data Architecture, Data Warehousing, Data Warehouse Design, Financial Data, Data Pipelines, Data Marts, Banking & Finance, Amazon Web Services (AWS), Amazon Athena, Data, ETL Development, Data Science, Docker Compose, Docker, APIs, Data Visualization, Databases, Data Build Tool (dbt), AWS Glue, Microsoft Excel, API Integration, Data Security, Reporting, AWS Lambda, Relational Databases, CI/CD Pipelines, Data Orchestration, Performance Optimization, ETL Pipelines, Database Schema Design, Kubernetes, Business Analysis, Amazon EventBridge, Data Integration, Batch
Data Engineer
2019 - 2021
Banco Original
- Migrated ten on-premises ETL processes to Google Cloud Platform using Google Cloud Storage, Cloud Functions, Google Pub/Sub, and Google BigQuery.
- Developed a system with Hive and Power BI for more than 20 financial products and around 100 campaigns monthly. This automatically gets the conversion rate of marketing campaigns, specifically email, push, and ads,.
- Created an API integration with BigQuery data to post on Facebook Marketing API and Google Ads using the Google Cloud Platform tools: BigQuery, Cloud Function, and Pub/Sub.
- Worked as a product owner and developer to create data pipelines and data modeling to support a newly acquired marketing platform, Oracle Responsys, and adapt it to the company.
- Worked with product managers to get the correct information on the transactional database and develop it ETL to a data warehouse.
Technologies: Apache Airflow, Agile, Google Cloud Platform (GCP), ETL, Google Pub/Sub, Google Cloud Functions, Google BigQuery, Facebook Marketing API, Google Ads API, ELT, Data Warehouse Design, Data Warehousing, Data Lakes, Financial Products, Financial Data, Product Owner, Oracle Responsys, Jupyter Notebook, Cloud Computing, Google Cloud Composer, Data Pipelines, Apache Hive, HDFS, Hadoop, Zeppelin, Python, SQL, Pandas, NumPy, Data Quality, Data Processing, PyCharm, Functional Programming, Data Engineering, Data Modeling, Data Architecture, Apache Spark, Google Cloud Storage, Data Marts, Banking & Finance, Data, ETL Development, Data Science, Data Visualization, Databases, Microsoft Excel, Data Governance, Data Scraping, Web Scraping, Website Data Scraping, API Integration, Data Security, Reporting, AWS Lambda, Relational Databases, Star Schema, Data Orchestration, Performance Optimization, ETL Pipelines, Database Schema Design, Business Analysis, Amazon EventBridge, Batch
Junior Business Intelligence Analyst
2017 - 2019
Banco Original
- Supported the development and structure of the data lake environment on HDFS.
- Developed fifteen ETL processes at data sources located on the data lake (HDFS). Delivered them on Hive for analytics purposes to business users.
- Worked with marketing managers to develop data-driven sales strategies through in-depth analysis of customers' behavior.
- Developed product performance dashboards to be accessed by the sales and product teams.
- Created more than 20 customer audiences for marketing campaign journeys according to product analytics rules.
- Improved the performance of a customer service chatbot through analytics on JSON files.
- Worked with business and product teams to disseminate analytics best practices, improve their query performance, and get the correct rules to achieve their analysis goals.
Technologies: HDFS, Hadoop, Zeppelin, Apache Hive, ETL, Chatbots, Data Analytics, Business Intelligence (BI), ELT, Financial Data Analytics, Financial Data, Financial Products, Marketing Automation, Jupyter Notebook, Microsoft Power BI, Marketing Campaign Design, Data Auditing, Sales Strategy, Data Lakes, Data Warehouse Design, SQL, Python, Data Quality, Data Processing, Data Engineering, Data Modeling, Data Warehousing, Data Marts, Banking & Finance, Data, ETL Development, Data Science, Data Visualization, Databases, Microsoft Excel, Data Governance, Data Security, Reporting, Star Schema, Data Orchestration, ETL Pipelines, Business Analysis, Batch
Business Intelligence Intern
2016 - 2017
Banco Original
- Created reports to analyze the rentability and acquisition of customers.
- Worked with product managers to audit ten financial products on the analytics databases and compare them with the transactional system.
- Developed around twenty ad-hoc queries with SQL and SAS using the SAS Guide software.
- Created around 25 top campaigns through several fonts of data (e.g., customers that delay the credit card bill).
- Developed the data dictionary for the databases of ten financial products.
- Supporting the survey and control of informational data gaps.
- Strong knowledge of flux, rules, and specifications of bank products, like credit cards, loans, and overdraft to audit the bases and create campaigns and reports.
Technologies: SQL, SAS Enterprise Guide, Dashboards, Excel 365, Marketing Automation, Financial Products, Data Analytics, Business Intelligence (BI), Financial Data, Financial Data Analytics, Banking & Finance, Data, Data Visualization, Databases, Microsoft Excel, Data Governance, Data Security, Reporting, Data Orchestration, Business Analysis
Experience
Post on BigQuery Data on Facebook Marketing API
Developed a project connecting BigQuery with the Facebook Marketing API, allowing data to be posted on Facebook Ads for building audiences and setting offline conversions. This integration facilitated deeper customer analytics and the application of machine learning models to enhance advertisement performance and devise new marketing strategies involving Facebook Ads.
Data Pipeline of an Open Dataset of a Brazilian Government Company Registry
This project is a data pipeline designed to load an open dataset into the Amazon RDS PostgreSQL database, sustain a back-end microservice, and use Amazon RDS for PostgreSQL by the Agendor website and mobile apps.
I built this process, which included assembling the Airflow environment on an EC2 with Docker, downloading and analyzing the open dataset of the Brazilian Government Company Registry on AWS S3, transforming the dataset into a table using AWS Athena, performing data quality validations, and loading the final table into a PostgreSQL table with Amazon RDS.
All the steps were created in an Airflow DAG scheduled to run weekly.
I built this process, which included assembling the Airflow environment on an EC2 with Docker, downloading and analyzing the open dataset of the Brazilian Government Company Registry on AWS S3, transforming the dataset into a table using AWS Athena, performing data quality validations, and loading the final table into a PostgreSQL table with Amazon RDS.
All the steps were created in an Airflow DAG scheduled to run weekly.
Data Integration and ETL for Financial System
Created a data pipeline using Apache Airflow that stores transactional data from a MySQL database into Amazon S3, transformed it into a data lake using Amazon Athena, and loaded the transformed and validated financial data into the ChartMogul financial system with data quality checks.
Customer Success Alert System
Developed a project for the Customer Success team to monitor customer login activity. I also implemented a system that generates alerts when a customer's login activity drops significantly, considering the behavior of each customer and generating the alert based on their standard deviation from it. This enables proactive customer engagement and retention efforts tailored to individual customer behavior patterns.
DynamoDB to Amazon Athena: Metabase Reporting Pipeline
Developed a robust data pipeline using Apache Airflow to efficiently transfer data from DynamoDB to Amazon S3 and Amazon Athena. I also implemented transformations on the resulting tables to generate reports on Metabase. These reports were used by the support team, providing them with valuable insights derived from the data.
Education
2014 - 2019
Bachelor’s Degree in Production Engineering
Federal Institute of São Paulo (IFSP-SPO) - São Paulo, Brazil
Certifications
OCTOBER 2020 - PRESENT
Modernizing Data Lakes and Data Warehouses with GCP
Coursera
SEPTEMBER 2020 - PRESENT
Google Cloud Platform Big Data and Machine Learning Fundamentals
Coursera
Skills
Libraries/APIs
Pandas, Google Ads API, Facebook Marketing API, NumPy, PySpark, TensorFlow
Tools
Apache Airflow, Amazon Athena, Microsoft Power BI, BigQuery, Tableau, GitLab, GitLab CI/CD, Retool, Microsoft Excel, PyCharm, SAS Enterprise Guide, Google Cloud Composer, Docker Compose, GitHub, AWS Glue
Languages
SQL, Python, Batch, Snowflake
Frameworks
Data Lakehouse, Delta Live Tables (DLT), Spark, Hadoop, Apache Spark
Paradigms
ETL, Agile, Business Intelligence (BI), Management, Functional Programming, UX Design
Platforms
Databricks, Jupyter Notebook, Google Cloud Platform (GCP), Oracle Responsys, Amazon Web Services (AWS), AWS Lambda, Zeppelin, Apache Kafka, Docker, Amazon EC2, Salesforce, Kubernetes
Storage
Data Lakes, Redshift, Data Pipelines, Databases, Relational Databases, Apache Hive, HDFS, Amazon S3 (AWS S3), Google Cloud Storage, PostgreSQL, MySQL, Data Integration, Google Cloud SQL, Microsoft SQL Server
Industry Expertise
Banking & Finance
Other
Data Quality, Data Engineering, Data Architecture, ETL Development, API Integration, Data Orchestration, Performance Optimization, ETL Pipelines, Google BigQuery, Google Data Studio, Data Analysis, Google Pub/Sub, Google Cloud Functions, APIs, Dashboards, Marketing Automation, Financial Products, Data Analytics, ELT, Data Warehousing, Cloud Computing, Agile Sprints, Financial Data, Financial Data Analytics, Marketing Campaign Design, Data Auditing, Data Marts, Data Processing, Data Modeling, Data Warehouse Design, Data, Data Science, Metabase, Data Visualization, Data Build Tool (dbt), Amazon Redshift, Data Governance, Data Scraping, Web Scraping, Website Data Scraping, Data Security, Reporting, Star Schema, CI/CD Pipelines, Database Schema Design, Business Analysis, Amazon EventBridge, Statistics, Economics, Engineering, Excel 365, Chatbots, Sales Strategy, Product Owner, Customer Success, Trading, Large Language Models (LLMs), Artificial Intelligence (AI), Machine Learning, RAG Pipelines, Retrieval-augmented Generation (RAG), OpenAI, Supabase, Vibe Coding, Data Cleaning, Row-level Security (RLS), B2C Marketing, Amazon RDS, Product Lifecycle Management (PLM)
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