Rezende

Independent Data Engineer & AI Architect

Self Employed • Trabajador autónomo

Jun 2026 - Present • 3 mos

As an independent Data Engineer and AI Architect, I partner with technical founders, marketing agencies, and e-commerce operations to build custom extraction pipelines and AI-driven data infrastructure. ​Transitioning my rigorous background in enterprise data auditing (Loss Prevention) into software engineering, I design automated architectures that prioritize data integrity, security, and precision. I don't just extract raw data; I engineer reliable, enterprise-grade ETL workflows that feed directly into client databases, CRMs, and executive dashboards. ​Core Architectural Deployments: • B2B Outbound Infrastructure: Harvesting stealth leads bypassing WAF/Anti-bot protocols and deploying AI-driven intent scoring. • E-Commerce Intelligence: Tracking competitor SKUs and stockouts to automate dynamic repricing logic. • Real Estate Aggregation: Multi-market property extraction with instant automated financial underwriting (NOI & Cap Rates). • SaaS Churn Capture: Parsing competitor reviews (Trustpilot/Yelp) to engineer targeted outbound pitch hooks. ​Technical Stack & Ecosystem: • ETL & Data Cleaning: Python, Pandas. • AI & LLM Routing: End-to-end integration with Groq, OpenAI, and localized models (DeepSeek, Llama, Qwen) for structured data processing. • Database Infrastructure: Secure synchronization with Supabase and PostgreSQL. ​I manage end-to-end agile deployments, ensuring 100% source code ownership and delivering production-ready market intelligence in under 24 hours.

Data & Risk Analyst (Loss Prevention)

Dia • Tiempo completo

Dec 2025 - Jun 2026 • 6 mos

Entrusted with high-stakes asset protection and financial auditing for a high-volume retail operation. In this role, I developed a rigorous, detail-oriented approach to data integrity, anomaly detection, and enterprise risk mitigation—skills that form the core foundation of my current Python ETL and AI data architectures. ​Key Responsibilities & Architectural Mindset: • High-Volume Data Auditing: Analyzed and cross-referenced massive daily transaction and inventory datasets to identify operational discrepancies, pinpointing anomalies to prevent financial leakage. • Risk Mitigation Logic: Structured reporting processes to track variance metrics, transforming raw, unstructured operational data into actionable security insights for executive management. • Process Optimization: Refined auditing workflows and operational protocols, establishing a zero-tolerance baseline for data inaccuracies. • Analytical Rigor: Cultivated a meticulous, audit-level approach to data validation. ​The Engineering Connection: This foundational experience in strict corporate compliance and data auditing is exactly what sets my current Python automation and data extraction services apart. I apply the same "Loss Prevention" mindset to data engineering: Ensuring high-fidelity extraction, building unbreakable anti-bot logic, and delivering datasets with 100% structural integrity. I don't just extract data; I guarantee its accuracy.