About me

I work across the AI data lifecycle, combining Data Engineering, Data Privacy, and AI data to build reliable, high-quality datasets for modern AI systems.

I am interested in the technologies, processes, and regulations that shape AI data - from collection, annotation, and validation to governance, privacy, and data generation.

I enjoy turning complex data challenges into practical, scalable solutions while sharing what I learn through speaking, writing, mentoring, and community initiatives.

Skills

Data Engineering
Data Lifecycle Management
Data Annotation
Data Privacy
AI Data Generation

Expertise

Python
SQL
Git
Jupyter, Redash
GDPR & Israeli Privacy Protection Law

Experience

Data Engineer

Designed, developed, and supported agentic data systems and AI-driven data workflows.
Built end-to-end AI data pipelines, including data collection, generation, validation, and annotation.
Developed LLM-powered workflows for database annotation, metadata enrichment, data quality validation, and process automation.
Designed scalable data infrastructure and databases supporting the complete AI data lifecycle.
Built automated data evaluation pipelines, defining quality metrics, validation processes, and monitoring frameworks.
Researched and implemented AI techniques for data generation, validation, and workflow optimization.

2022 - PRESENT

Data Operations

Managed end-to-end AI data operations, ensuring high-quality datasets for machine learning applications.
Led and mentored data annotation teams, defining workflows, quality standards, and annotation guidelines.
Monitored and analyzed data quality metrics to improve dataset consistency and annotation accuracy.
Designed and automated data operational workflows to improve efficiency and reduce manual effort.