I'm Kiran Babu Thatha — a senior SEO and product analyst with 8+ years shaping organic strategy across high-growth e-commerce and tech platforms. I work where technical SEO, data, and AI intersect: from millions of pages on global stores to ML-powered internal linking and LLM-driven content systems.
Eight years of receipts: shipped products, recovered revenue, and growth measured against real KPIs — not vanity metrics.
My background sits at the seam between SEO strategy and applied data engineering. I started as a support engineer in banking and HR systems — writing SQL, automating thousands of manual workflows — before moving to Berlin for a Master's in Digital Engineering and joining Wayfair, where I scaled from intern to Senior Product Analyst.
At Wayfair, I owned organic growth across multiple international markets, partnering with Engineering, Product, and Content to align organic visibility with revenue. I shipped ML-based sentence-transformer models for internal linking, built a Python AI system for multilingual content generation, and ran end-to-end experimentation programs — 15+ A/B tests, full statistical design through rollout decisions.
What I care about: scale without manual toil. Most of my work ends in an automated pipeline, a self-service tool for non-technical teams, or a dashboard that replaces a recurring meeting. I'm bullish on AI-driven search (GEO/LLM optimisation) as the next layer of the discipline — and I've been integrating OpenAI and Anthropic APIs into production SEO workflows since well before it was the default.
Outside of work I'm a hackathon person — runner-up at the Cyprus Hackathon for Workwise, an AI agent app — and I read widely on culture, travel, and how systems get built.
From banking ETL pipelines in Hyderabad to multilingual SEO at Wayfair Berlin — every role has been about scale, automation, and decisions backed by data.
Brief case studies from my work at Wayfair — the problem, the approach, and the numbers that made it worth shipping.
Replaced rule-based internal linking with an ML pipeline using sentence-transformer embeddings to surface the most semantically relevant link targets. Demonstrated AI-driven search optimisation in a production environment.
Built an MVP self-service tool that proactively monitored site health and blocked harmful changes to high-performing pages — letting non-SEO teams ship safely without bottlenecking on the SEO team.
Python + NLP + RegEx pipeline to detect and resolve duplication and cannibalisation across EN, DE, and FR content — turning a recurring quality problem into a one-time engineering fix.
Designed and led product direction for a Python-based content workflow using OpenAI LLMs — aligning generated copy with searcher intent, brand voice, and the marketing funnel across multiple global stores.
Six focused offerings drawn from production work at Wayfair. Engagements run as one-off audits, fixed-scope projects, or month-to-month retainers.
Deep-dive crawl, indexation, and architecture reviews using Screaming Frog plus custom Python crawlers. Prioritised remediation roadmap with engineering- ready tickets.
Hreflang strategy, market prioritisation, and localised content frameworks. Built on years of running SEO across global stores in multiple languages.
Templated page systems, LLM-assisted content generation, and AI-search (GEO) readiness. Scale your content footprint without scaling your team.
End-to-end test design — hypothesis, KPI definition, statistical sizing, and post-test analysis. Build an SEO experimentation muscle that converts opinions into evidence.
GA4, Search Console, BigQuery, and Looker / Tableau dashboards built for self-service. Replace recurring reporting meetings with always-on KPIs.
Custom crawlers, content-quality monitors, internal-linking engines, and guardrail tooling. Anywhere your team is doing the same thing twice, I'll help you automate it.
A working snapshot — not an exhaustive list. Tools earn their place by appearing in something I shipped.