Senior Data Engineer

• Development of data pipelines for A/B testing framework supporting millions of users
• Setting up CI/CD chain with GitHub Actions and Jenkins for automated deployments
• Airflow DAGs implementations for workflow orchestration and data pipeline management
• Building AWS and Databricks infrastructure with Terraform for scalable data processing

As a member of the MOSAIC team, we developed a platform for detecting fraud across various payment methods such as checks, instant payments, and wire transfers. I was responsible for the check fraud perimeter, working on the design and implementation of scoring pipelines under a microservices architecture, in both batch and streaming modes.

• Scalable Data Pipeline Development: Built scalable data pipelines for parsing, profiling, and scoring financial transactions under a microservices architecture, supporting both batch and streaming processing modes
• Application Maintenance & Evolution: Maintained and evolved applications handling large-scale payment data, ensuring system reliability and performance for fraud detection operations
• Agile Collaboration: Represented the check fraud perimeter in agile ceremonies and daily stand-ups, facilitating cross-functional communication and alignment on fraud detection initiatives
• Machine Learning Implementation: Developed and deployed a machine learning model for automated check fraud detection with high accuracy, contributing to the platform's fraud prevention capabilities
• Stakeholder Reporting: Produced weekly reports to update stakeholders on progress and planned actions, ensuring transparency and alignment on fraud detection project milestones

• Responsible for data platform managing over 7 terabytes of data based on ELK stack
• Developed and maintained systems for collecting, storing, and making available internally produced data
• Conducted POCs on data tools and implemented new data platform architecture (Lambda architecture)
• Developed machine learning engines for customer segmentation and retention
• Productionized machine learning engines via REST API with Flask
• Developed WhatsApp and Telegram bots in Node.js for customer engagement
• Supported newcomers and interns through mentorship and technical guidance

• Analysis of thesis chapter showing construction rules for a class of graphs
• Description and implementation (OCaml) of algorithm for random generation of graphs
• Conducted research on graph theory applications and algorithmic complexity