I provide focused, senior-level reviews of existing data engineering and machine learning systems with the goal of improving reliability, scalability, and cost efficiency. My work covers end-to-end data pipelines, ETL processes, ML training and inference workflows, and cloud infrastructure supporting production workloads.
The service is designed for startups and engineering teams running real-world data or ML systems that need an experienced second opinion. I analyze architecture, data flow, failure modes, performance bottlenecks, and operational risks, and then deliver concrete, actionable recommendations that can be implemented immediately. Reviews typically include guidance on data quality, pipeline robustness, deployment patterns, cloud cost optimization, and system simplification.
This service is best suited for teams who want practical improvements, not theoretical advice or generic best practices.