Daniel Podgaichenko

Developed enterprise-level automation systems (Python + AI/ML) that eliminated 80% of manual SEO operations and strengthened diagnostic accuracy across global properties. Built high-scale GSC, log-processing, and monitoring pipelines to track indexation, rendering, and anomalies, reducing QA cycles by 90%, and enabling real-time technical visibility. Led cross-functional initiatives with engineering and product teams to design scalable crawling, auditing, and structured data frameworks, supporting billions of monthly impressions. Implemented experimentation workflows and performance dashboards that boosted organic KPIs, increased iteration speed, and improved system reliability end-to-end.

Led the global technical SEO strategy for multi-region, high-traffic ecosystems, driving 200% organic growth through architectural improvements to indexability, rendering pipelines, and site-wide SEO systems. Designed automated reporting and data pipelines (Looker, GA, GSC APIs) that reduced manual reporting workloads by 60% and enhanced the accuracy of enterprise-scale analytics. Improved Core Web Vitals by 50–70% across major properties through engineering collaboration on performance diagnostics and code optimization. Scaled structured data and internal linking systems across multi-million URL properties, enabling richer SERP eligibility and competitive search positioning.

Led SEO architecture, crawl logic, and indexation systems for high-traffic properties, improving crawl efficiency and index coverage by 50%. Built scalable audit frameworks and technical QA workflows embedded into engineering sprints, increasing implementation accuracy, and accelerating deployments. Developed long-tail and entity-driven strategies optimized for large content networks, generating consistent double-digit visibility growth across global markets. Acted as the technical SEO liaison for the product, engineering, and marketing teams, ensuring aligned priorities and KPI-driven execution.