Hello, I'm Alexandre

Tech PMO | Technical Lead | Analytics Engineer

I structure data architecture, executive KPIs and Python automations to turn complex operations into evidence-driven management. I work with SQL, BI, APIs, governance and technical leadership.

Curitiba/PR, Brazil Hybrid/Remote Tech PMO • Analytics Engineer • Data Lead
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Alexandre

About Me

Get to know my journey and experience

I'm a Tech PMO, Technical Lead and data professional with experience in data architecture, analytics, BI and process automation. Today I work at SODITECH structuring an internal solution for HR management, technical workforce planning and operational indicators.

My background spans senior consulting, banking, benefits, call center, insurance and logistics, always connecting technology, data and operations. I use Python, SQL, Power BI, Databricks, APIs and governance to standardize metrics, reduce inconsistencies and support executive decisions.

15+ Years in Technology
93% Data Divergence Reduction
D+3 Automated Closing Cycle
Working with data

Professional Focus

Where I connect technology, data and operational management

Primary Focus

Tech PMO & Data Technical Leadership

Structuring internal solutions, indicator governance and management routines for business and technology areas. I work on architecture definition, stack selection, integrations and management models focused on HR, technical workforce, projects and operational performance.

Core Capabilities:

  • PMO implementation and technology governance
  • Data architecture and open source stack definition
  • Source integration, APIs and analytics modeling
  • People, recruitment, training, SLA and project KPIs
  • Skill matrices, talent pools and knowledge management
  • Alignment across operations, leadership and technical teams
Python SQL APIs Power BI Dashboards PMO Data Governance Open Source
Secondary Focus

Analytics Engineering & Executive BI

Building pipelines, metric standards and executive dashboards to reduce noise, improve number reliability and accelerate decision-making. Experience across banking, benefits, consulting, call center, insurance and logistics operations.

Core Capabilities:

  • Corporate data consolidation and single-source metrics
  • Automated reporting, auditing and management closing
  • Web dashboards with Python, Streamlit, Plotly and Power BI
  • Funnel validation, source auditing, UTMs and business rules
  • SQL Server, Oracle, Databricks and relational databases
  • Analytical storytelling and technical support for analysts
SQL Server Oracle Databricks Streamlit Plotly DAX Git CI/CD

Professional Experience

My journey and key accomplishments

Technology PMO & Technical Lead

SODITECH | Engineering & Technology March 2026 - Present
  • Responsible for the data architecture and technology stack of an internal solution for HR management, technical workforce planning and operational indicators.
  • Lead the structuring of people, recruitment, training, absenteeism, vacation, turnover, hours, SLA, technical and economic project indicators.
  • Work with source integration, API consumption, Python dashboards and governance for skill matrices, talent pools and knowledge management.
Python SQL APIs Dashboards PMO Data Governance

Senior Data Consultant

Lyx Participações November 2025 - April 2026
  • Redesigned the analytics workflow, replacing Power BI panels with Python, Streamlit and Plotly web dashboards supported by Python + SQL data pipelines.
  • Standardized tables and KPIs, implemented consistency checks, audit logs, scheduled routines and automated alerts for anomalous variations.
Python SQL Streamlit Plotly Data Quality

Senior Data Analyst

Paraná Banco S/A August 2025 - October 2025
  • Validated integrated funnels across digital and phygital lines, mapping the lead-to-conversion journey and aligning business rules across departments.
  • Audited GA/UTM sources, reconciled real versus reported numbers and designed unique metrics for more reliable executive consumption.
SQL SQL Server Databricks Marketing Analytics Data Governance

Commercial Planning Analyst

VR Benefícios December 2022 - June 2025
  • Consolidated corporate datasets and indicators for proposals, activation and new accounts, reducing data divergences by 93%.
  • Automated reporting and parameterization routines, accelerating the closing cycle from D+10 to D+3 and centralizing critical KPIs in operational dashboards.
  • Developed Python and BI integrations for system synchronization, reducing manual errors and strengthening data-driven culture.
Python Oracle Azure Databricks Power BI KPIs

Senior Information Technology Professional

Sudacall June 2020 - November 2022
  • Led a data-driven program with conversion, AHT, absenteeism, sales per operator and CPC KPIs, generating +17% production in 3 months and an additional +18% in 12 months.
  • Consolidated workloads with Docker and cloud, high availability, balancing and disaster recovery, reducing relative IT cost.
  • Automated extraction, quality and integration in Python to reduce indicator latency and support daily operational and campaign decisions.
Python Docker Cloud KPIs Technical Leadership

Technology Stack

Tools and platforms I master

Languages

Python
9+ years
SQL
6+ years
JavaScript
3+ years

Data Science & ML

Pandas
Expert
Scikit-learn
Advanced
TensorFlow
Intermediate
NumPy
Advanced

Visualization & BI

Power BI
Expert
Matplotlib
Advanced
Plotly
Advanced

Data Engineering

Apache Airflow
Advanced
Docker
Advanced
dbt
Advanced

Databases

PostgreSQL
Advanced
SQL Server
Advanced
Oracle
Advanced
Azure Databricks
Advanced

Cloud & DevOps

Google Cloud
Intermediate
Git
Advanced
CI/CD Pipelines
Advanced

Technical Skills

How I interact with technologies in my daily work

alexandre@skillset:~$
Python
>>> if isinstance(alexandre, PythonExpert) and alexandre.experience > 5: return "Automation, analysis, and APIs? Just call me!"
Automation, analysis, and APIs? Just call me!
SQL
sql> SELECT skill, proficiency FROM expertise WHERE developer = 'alexandre' AND skill LIKE '%SQL%' HAVING proficiency = 'Advanced' /* Optimized query, indexes used! */
skill: SQL | proficiency: Advanced
Pandas
>>> df = pd.read_csv('challenge.csv') df.groupby('problem').apply(alexandre.solve) # Result: insights ready for decision-making
DataFrame transformed successfully!
Docker
$ docker build -t alexandre/solution:latest . && docker run --rm -e PROBLEM=complex alexandre/solution:latest
Container running... Problem solved!
Power BI
DAX> CALCULATE([Insights], FILTER(ALL('Projects'), [Author]="Alexandre" && [Level]="Advanced" ) )
Interactive dashboard created!
Git
$ git commit -m "feat: robust solution delivered by Alexandre" && git push origin main
[main abc123] feat: robust solution delivered by Alexandre
MongoDB
> db.skills.aggregate([ { $match: { user: "alexandre", skill: "MongoDB" } }, { $project: { proficiency: 1, pipelines: 1 } } ])
{ "proficiency": "Advanced", "pipelines": "Expert" }
Airflow
>>> with DAG('alexandre_pipeline', schedule_interval='@daily') as dag: run_etl = PythonOperator( task_id='run_etl', python_callable=alexandre.advanced_etl )
Airflow pipeline active and monitored!
Matplotlib
>>> import matplotlib.pyplot as plt plt.style.use('alexandre_custom') plt.plot(data, color='insights') plt.title('Another visualization that tells a story')
Chart saved: storytelling_with_data.png
Machine Learning
>>> from sklearn.ensemble import RandomForestClassifier model = alexandre.train_model(data, target) print(f"Accuracy: {model.score(X_test, y_test):.2%}")
Accuracy: 94.7% - Model ready for production!

Certifications

Validations of my technical competencies

Pulling the latest badges from Credly...

Verified competencies

Automatically synced from Credly

Mapping recognized skills...

Featured Projects

Demonstration of technical rigor and production

Flagship Projects

6 main projects that demonstrate technical rigor, reproducibility, and production focus

🌟 Flagship
Data Engineer Case - DataOps MongoDB Python

Data Engineer Case - DataOps Pipeline

Complete data processing system with DataOps pipeline that integrates Python and MongoDB, offering full support for local, remote, and Docker environments with advanced ETL/ELT features.

Reproducible Documented Tests CI/CD Limitations

How to Run

  1. Clone the repository: git clone https://github.com/alex-des-santos/Case-Engenheiro-dados
  2. Set up MongoDB or use Docker: docker-compose up -d
  3. Run the pipeline: python main.py
  4. Access the full documentation in the README

Impact & Decisions

  • Decision: Automated ETL/ELT pipeline with data validation and multi-environment support
  • Benefit: Reliable data processing with guaranteed quality through programmatic validations
  • Trade-off: Initial setup complexity vs flexibility for local/remote/Docker environments
Python MongoDB Pandas Docker DataOps
🌟 Flagship
KPIs Governance Dashboard

KPIs Governance Dashboard

Executive dashboard for data governance and strategic KPIs, with modern lakehouse architecture and automated ETL/ELT pipeline for decisions based on reliable data.

Reproducible Baseline Documented Tests Limitations

How to Run

  1. Clone: git clone https://github.com/alex-des-santos/kpis-governance-dashboard
  2. Install dependencies: npm install or pip install -r requirements.txt
  3. Run ETL/ELT pipeline (Airflow or script)
  4. Open dashboard (Power BI or Metabase)

Impact & Decisions

  • Decision: Which KPIs to prioritize for data governance and quality
  • Benefit: Centralized visibility of business metrics with guaranteed quality
  • Trade-off: Update latency vs quality assurance (currently simulated data)
Data Engineering Apache Airflow dbt Data Governance Great Expectations Power BI
🌟 Flagship
Adventure Works - Executive Data Analysis

Adventure Works - Executive Data Analysis

Executive dashboard for financial analysis of Adventure Works with revenue forecasts using Machine Learning and market opportunity analysis.

Reproducible Baseline Documented Validation Limitations
94.7% Accuracy (vs. naïve baseline: 78%)
23% Growth Opportunity

How to Run

  1. Access the online dashboard or download the .pbix file
  2. Open in Power BI Desktop (free version)
  3. Update datasources if necessary
  4. Explore executive views and ML forecasts

Impact & Decisions

  • Decision: Expansion to Brazilian market based on opportunity analysis (23% potential growth)
  • Benefit: Revenue forecasts 17% more accurate than naïve forecast for strategic planning
  • Trade-off: Accuracy vs interpretability (complex model vs simple rules for executives)
Power BI Machine Learning SQL Business Intelligence DAX
🌟 Flagship
CSV Insights Tool

CSV Insights Tool - GenAI for Analytics

Interactive web tool that uses generative AI for automatic analysis of CSV files, generating statistical insights, visualizations, and recommendations in real time.

Reproducible Documented Evaluation Security Observability
70% Time Reduction vs Manual
<3s Processing 500MB

How to Run

  1. Access online tool (no installation)
  2. Upload CSV file (up to 500MB)
  3. Wait for automated analysis
  4. Explore AI-generated insights, visualizations, and recommendations

Impact & Decisions

  • Decision: Prioritization of exploratory analyses and quick pattern identification
  • Benefit: 70% time reduction vs manual analysis with Pandas/Excel for new datasets
  • Trade-off: Speed vs accuracy (insights may hallucinate - manual validation recommended)
JavaScript GenAI D3.js Data Visualization OpenAI
🌟 Flagship
ML Production Pipeline - MLOps with FastAPI and MLflow

ML Production Pipeline - Complete MLOps

End-to-end Machine Learning production pipeline with experiment tracking (MLflow), inference API (FastAPI), and containerized infrastructure. Wine quality classification with 88% F1 Score.

Reproducible Documented Tests CI/CD Observability
88.2% F1 Score
90.9% ROC AUC
12 Experiments

How to Run

  1. Clone the repository: git clone https://github.com/alex-des-santos/ml-production-pipeline
  2. Start the services: docker compose up -d
  3. Train the model: docker exec ml-api python3 train.py
  4. Access MLflow UI at localhost:5000 and API at localhost:8001/docs

Impact & Decisions

  • Decision: MLflow for full experiment tracking and model versioning
  • Benefit: Total reproducibility - any experiment can be recreated or rolled back
  • Trade-off: Infrastructure complexity vs model governance and auditability
Python FastAPI MLflow Docker scikit-learn Prometheus
🌟 Flagship
Case Study: Car Insurance Analysis

Car Insurance Analysis - Forecast & Dashboard

Complete case study focused on the automotive insurance sector, covering Exploratory Data Analysis (EDA), advanced predictive modeling, and the creation of an interactive web dashboard.

Reproducible Documented EDA

How to Access

  1. Interactive App: Access the Streamlit Dashboard
  2. Main Analysis: View on GitHub
  3. Kaggle Notebook: Check the EDA on Kaggle
  4. Backend/App: Check the Streamlit App Repository

Impact & Decisions

  • Decision: Development of a cloud-based web dashboard
  • Benefit: Interactive presentation of predictive data for end-users
  • Trade-off: Fast deployment with Streamlit vs full UI/backend customization
Python Streamlit Machine Learning Pandas Scikit-learn
🌟 Flagship
Overview Dashboard
Products Dashboard
Customers Dashboard
Funnel Dashboard
Financial Dashboard
Swipe

Ecommerce Analytics - Metabase Dashboards

Python automation that programmatically generates dozens of analytical dashboards in Metabase using its REST API, delivering instant insights on Sales, Customers, Funnel, and Finance for E-commerce.

Automated Documented Requirements

How to Run

  1. Clone: git clone https://github.com/alex-des-santos/ecommerce-analytics
  2. Install libs: pip install requests python-dotenv
  3. Set credentials in .env (Metabase URL and API Key)
  4. Run: python scripts/create_ecommerce_dashboards.py

Impact & Decisions

  • Decision: Programmatic creation via API instead of manual UI clicks
  • Benefit: Instant deployment of 50+ analytical cards in seconds, ensuring standardized templates
  • Trade-off: Needs Python script maintenance vs drag-and-drop ease of Metabase UI
Python Metabase API REST API Data Analytics Automation
Data Scientists Guide

Data Scientists Guide

Open guide to build a Data Science portfolio with checklists, templates, and practical references.

Mentoring Documentation Python Best Practices
Habits and Performance Analysis

Habits and Performance Analysis

Study crossing habits and performance of students using notebooks, visualizations, and statistical experiments.

Python Pandas EDA Statistics
AI Resume Optimizer

AI Resume Optimizer

Engine that compares job openings with CVs, suggests improvements, and feeds the official Chrome extension for automated optimization.

NLP Python Career Chrome Extension
Machine Learning Algorithms

Learn Machine Learning Visually

Interactive educational platform to teach machine learning concepts through dynamic visualizations and accessible practical examples.

JavaScript D3.js Educational Tech Interactive Learning
Windows Event Log Analyzer

Windows Event Log Analyzer

Modern and intuitive web tool for analyzing Windows Event Viewer logs, with intelligent insights generated by AI using Google Gemini for automatic identification of critical issues.

JavaScript Google Gemini AI CSV Analysis System Monitoring

Projects in Development

✅ Completed

End-to-End ML in Production

Complete Machine Learning pipeline from training to production: tracking with MLflow, REST API with FastAPI, Docker containerization, automated CI/CD and data drift monitoring.

MLflow • FastAPI • Docker • Scikit-learn • Pytest • GitHub Actions • Prometheus
Gap: MLOps & ML in Production
View Project GitHub
📋 Planned Q1 2026

Real Analytics Engineering

Modern lakehouse architecture: public data ingestion → dbt (staging/intermediate/marts) → Great Expectations for data quality → versioned metrics → executive dashboard with BI.

dbt Core • Great Expectations • Apache Airflow • DuckDB/BigQuery • Power BI
Gap: Data Governance & Data Contracts
📋 Planned Q1 2026

GenAI with Rigorous Evaluation

RAG system for technical documentation with traceable citations, automated evaluation (RAGAS), security hardening (prompt injection), complete telemetry and regression tests.

LangChain • ChromaDB • RAGAS • OpenAI • FastAPI • Guardrails • LangSmith
Gap: GenAI/RAG in Production with Rigor

More Projects on GitHub

Complete list of my own repositories (no forks) focused on AI, data, automation, and productivity.

Repository

BackupContainers

Bash script for automated backup of Docker configurations, volumes, and databases directly to GitHub.

Docker Bash Automation
Open on GitHub

Repository

data-quality-framework

Reusable and scalable Data Quality Framework for validating data pipelines using Great Expectations.

Data Quality Great Expectations Data Engineering
Open on GitHub

Repository

agentdemo24por7

24/7 multi-agent orchestrator that triages tickets autonomously with LangGraph/LangChain state machines.

LLMs Agents Automation
Open on GitHub

Repository

alex-des-santos

Interactive README that highlights mission, principles, and professional roadmap.

Portfolio Markdown Community
Open on GitHub

Repository

analise-habitos-desempenho

Research that connects students' habits and performance using notebooks, visualizations, and statistical experiments.

Python Pandas EDA
Open on GitHub

Repository

Case-Analista-Dados

Adventure Works executive dashboard with forecasting, What-If analysis, and Brazil-specific storyline.

Power BI Forecast Business
Open on GitHub

Repository

Case-Engenheiro-dados

DataOps challenge with Python + MongoDB pipelines, Dockerized environments, and full operational docs.

DataOps MongoDB Docker
Open on GitHub

Repository

csv-insights-tool

Browser-based CSV analyzer that produces instant statistics and D3.js charts for huge files.

JavaScript D3.js Data Viz
Open on GitHub

Repository

dash-desmatamento-fogo-chuva

Plotly Dash dashboard that cross-analyzes deforestation, fire spots, and rainfall across Brazilian biomes.

Dash APIs Sustainability
Open on GitHub

Repository

datascients-guide

Open guide to build a compelling data science portfolio with checklists, templates, and curated references.

Mentoring Documentation Python
Open on GitHub

Repository

encceja-pandemia-impact-analysis

Pandemic impact analysis on ENCCEJA results combining public datasets and notebook storytelling.

Pandas Education Storytelling
Open on GitHub

Repository

kpis-governance-dashboard

End-to-end data governance case with strategic KPIs, architecture diagrams, and BI deliverables.

Data Governance dbt Power BI
Open on GitHub

Repository

machine-learning-alg

Visual playground that explains ML algorithms with interactive demos and intuitive charts.

JavaScript Education ML
Open on GitHub

Repository

network-troubleshooting

Interactive guide that walks home users through network diagnostics via dynamic Q&A flows.

UX Web App Networking
Open on GitHub

Repository

promptsections

Streamlit app that breaks long Stable Diffusion/ComfyUI prompts into reusable sections for faster iteration.

Streamlit Stable Diffusion Generative AI
Open on GitHub

Repository

resume-otimizator

Engine behind the AI Resume Optimizer that compares job posts vs. CVs and powers the Chrome extension.

NLP Career Chrome Ext
Open on GitHub

Repository

whatsapp-rpg-gm

Automated Dungeon Master for WhatsApp that manages sheets, dice rolls, and full D&D storytelling with AI.

Python Bots RPG
Open on GitHub

Repository

windows-event-analyzer

Web tool that interprets Windows Event Viewer logs, flags critical signals with AI, and speeds up troubleshooting.

Observability Gemini AI Logs
Open on GitHub
Browse all repositories

Let's Talk?

Get in touch for discussions about projects and opportunities

LinkedIn

Connect with me

alex-des-santos

GitHub

See my projects

alex-des-santos

Email

Send me a message

eu@alexandre.pro