Mayna K.
SEO Specialist
SUMMARY OF EXPERIENCE
• 5+ years in digital marketing with a strong and consistent focus on SEO – spanning content strategy, technical SEO, and more recently AI-powered search (AEO, GEO, SXO, AIO) at her current role with Digital Rocket IO in Dubai, where she's working on luxury real estate accounts across the UAE.
• During her time at AIMLEAP and Zivanta Analytics (covering roughly 2.5 years), she managed multi-client SEO and content portfolios across SaaS, real estate, and e-commerce, building solid breadth across industries and sharpening her ability to run data-driven strategies at scale.
• Currently consulting independently for multiple clients – including a luxury interior design brand expanding into the UAE market, an e-commerce beauty brand (full lifecycle marketing via Customer.io and AISensy), and an online education YouTube channel, demonstrating a genuine ability to manage diverse briefs and deliver end-to-end.
👍 What we loved about them
• Understands the nitty gritty details – Her concept of "extractable facts" – making sure content contains specific, scannable details like founding years, material types, and price points rather than vague heritage – reflected an understanding of how AI models actually pull citations. She didn't just say "you need to structure your content well"; she was able to confidently describe the specific signals that influence whether a page gets referenced or ignored, and she was able to apply that thinking to a product page in a practical way.
• Strong professional judgement – She switched her competitive benchmark from Burberry to Brunello specifically because of how Brunello was performing in AI citations, not because of brand prestige or market positioning, which was a well considered and pragmatic call. She instantly understood that the assessment was really about GEO visibility and reoriented her whole analysis around that. It's a small detail, but it says something about how she reads a brief and whether she's thinking or just going through the motions.
• Calm and clear under challenge: Throughout the technical interview, we pushed back on her reasoning several times, and Mayna didn't get flustered or defensive. She adjusted her explanations based on the follow-up questions, acknowledged limitations where they existed, and kept the conversation moving, which is a reasonable indicator of how she'd handle a tough client call or an internal review where her recommendations are being scrutinised.
• Experience with automation: She has used N8N in her current role to automate SEO tasks including outreach, reporting, and data pulls from Google Analytics and Search Console. For a role that will eventually need to think about scale across multiple client accounts, having someone who's already built automation workflows is a meaningful starting point.
ℹ️ Things to be aware of
• She's an immediate joiner in a part-time capacity.
• She can translate SEO requirements into clear developer briefs without needing hand-holding. Specifically, on custom-coded websites with no CMS or plugins to lean on, she prepares schema markup separately for each page, documents issues with step-by-step instructions, and sends developers reference links alongside the brief.
• Most of her content work has been for B2B or service-based businesses, and the D2C/e-commerce experience she does have is fairly limited. The GEO and technical SEO side transfers well, but the content sensibility for luxury fashion is something she will require time to develop.
💁♀️ Where he may need support
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Although he lacks extensive experience with LinkedIn and Bing ads, his proficiency in Google ads suggests a high adaptability to new platforms.
👩💻 Technical interview performance
Objective
This candidate was invited to a 60-minute follow-up interview to assess their technical capabilities in more detail. During this interview, we assessed their critical-thinking skills, technical expertise, and overall conversational skills.
Technical abilities
• AI search optimisation & content architecture [8/10]: Mayna has a strong grasp of this area, and it came through fairly clearly during the interview. She tested across both Perplexity and ChatGPT using a real query, and she understood why the two platforms return results differently. Her concept of "extractable facts" reflects a real understanding of how AI models pull citations, and she backed it up with worked examples. She also handled the intent-based vs keyword-based distinction well, using a concrete example to explain that even without a literal keyword appearing in the copy, surrounding signals can still communicate the right intent to an AI model. Overall, her thinking here felt practical and applied.
• Structured data & technical implementation [8/10]: Technically, Mayna is very capable. She ran a full Screaming Frog audit, surfaced three distinct issue types (hreflang errors, missing H1 tags, and pagination handling) and, more importantly, she prioritised them with a clear rationale rather than just listing them. Her hreflang explanation showed she understands the real-world impact of these errors on international user experience, not just the technical consequence. Her schema knowledge is applied and practical too; she correctly identified product schema as the highest-priority implementation in a luxury e-commerce context, and she explained why review and pricing schema can nest inside it rather than being treated as separate workstreams. She's also clearly done implemented this on real client projects, including international rollouts from scratch. Her limitation is that she works with dev teams rather than executing backend fixes herself, but she documents and guides implementation well, and that's a reasonable expectation for this type of role.
• AI-powered content production [7.5/10]: Mayna uses AI tools with genuine purpose and intention. She's mapped out a deliberate workflow; ChatGPT for research because of its dedicated research agent mode, Perplexity for competitive comparisons because it returns structured tables naturally, and Claude for formatting, slide creation, and document output. She even used Claude to build the actual presentation she walked through in the interview, which shows her skills in practice rather than just a stated capability. She also shows good instincts around AI limitations, and she's clear that she doesn't trust AI tools for search volume or KD data and uses SEMrush to validate those numbers instead.
• Entity SEO & topical strategy [7/10]: Mayna shows a good working understanding of semantic and entity-level thinking, though it's more intuitive than formally structured. Her instinct to recommend granular, specific product details like materials, craftsmanship origins, and founding dates is solid entity SEO practice, even if she didn't explicitly frame it in those terms. She also touched on using related and semantic keywords to expand around a core term, and her explanation of how AI models understand intent beyond literal keywords shows she gets the broader concept of how semantic meaning works in modern search. Her competitor selection logic, choosing Brunello based on AI citation frequency rather than conventional brand proximity, also shows she can think about topical authority in a non-traditional way.
• Search behaviour analysis & performance tracking [7/10]: Mayna's reporting and tracking setup is sound and practical. She has automated dashboards pulling directly from Google Analytics, runs weekly client check-ins, and uses SEMrush to anchor her data in validated numbers rather than approximations – that's a reliable baseline, and it shows she thinks about reporting as something that should run in the background rather than eat up manual time. She also mentioned that Google Search Console now has its own AI layer and that she's comfortable working within that, suggesthing that she keeps up with how tools are evolving. Her approach to monitoring AI platform performance is a bit more manual; she tests prompts directly in Perplexity and ChatGPT and observes what surfaces, which is a reasonable approach given how early-stage AI search tracking still is.
Areas of growth
• She should strengthen her prioritisation justifications with data rather than assumptions. When she flagged H1 tags as high priority, she acknowledged her reasoning and rationale was based on assumption rather than confirmed data. This wasn't a recurring theme across the interview, but it's the one moment where the rigour slipped a little.