Roman

Roman Mikhieiev
Fractional SEO Lead | Search Systems & Technical SEO
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📍 Vilnius, Lithuania

Professional Summary
SEO Professional with 10+ years of experience driving scalable organic growth
through technical SEO, content strategy, and search visibility optimization.

Approaches SEO through a systems engineering lens, understanding search
engines as large-scale distributed information systems with graph-based
structures and ranking pipelines, and applying this model to organic growth
strategy and execution, consistently driving scalable organic performance
across large-scale digital products.

Expertise in optimizing information architectures for both SERP and emerging
AI-assisted discovery systems (RAG and Reasoning Models). Leverages a
deep understanding of two-tier retrieval architectures to build high-information-
density systems, develop connected entity graphs (JSON-LD), improve content
retrievability, and align websites with evolving search and AI interpretation
layers.

Key Achievements:
• Achieved Top 1–2 Google rankings for highly competitive search queries in
European, CIS, and U.S. markets.
• Scaled a content platform from 0 to 3.3M+ total clicks, reaching 2M+ organic
visits within a 28-day period with a 16.2% average CTR.
• Managed high-risk enterprise e-commerce migrations, eliminating legacy
technical debt and scaling post-migration visibility without critical traffic loss.

• Led large-scale SEO audits and optimization programs across enterprise and
high-traffic digital properties, delivering 2x–10x improvements in organic traffic
and conversion performance.

• Engineered AI-native optimization frameworks that established the website as
a recurring top-cited source in Google AI Overviews across core business
 verticals while maintaining top organic rankings, contributing to increased
visibility, higher-intent traffic acquisition and stronger revenue efficiency.

• Managed full-cycle SEO campaigns, spanning technical optimization, content
strategy, authority building, outreach, and conversion rate optimization.

Core Expertise:
Technical SEO & Audits, SEO Strategy, Semantic & Entity-Based SEO,
Information Architecture, Site Migrations, Conversion Rate Optimization,
Internal Linking & Taxonomy Design, Generative Search Optimization,
Structured Data (Schema.org / JSON-LD), Link Acquisition Strategy, Data
Analysis & API Integrations, Digital PR & Strategic Outreach

Tools & Platforms:
Screaming Frog, JetOctopus, Ahrefs, Semrush, Python, Google Search
Console, PageSpeed Insights, Google Analytics 4, InLinks, MarketMuse,
BigQuery, SQL, Looker Studio, Redis, ELK Stack, Server Log Analysis, LLM-
powered workflows

SEO Automation & AI Systems:
Experienced in SEO automation, API integrations, Python-driven data analysis,
and large-scale search intelligence systems. Skilled in designing information
architectures, entity graphs, and structured data frameworks optimized for LLM
discovery, AI Overviews, and retrieval-augmented search systems.

Education:
Master’s degree in applied physics with academic studies in computer science,
mathematics, programming, and data analysis.

Languages:
English (Professional Working Proficiency), Ukrainian, Russian.
Professional Experience

Fractional SEO Lead / Search Systems Strategist
Long-Term Embedded Engagements | Full-Time
2023 – Present

 Engagement model: Embedded SEO leadership and interim SEO roles
within cross-functional product teams (typically 6–18 month engagements).

 Served as an embedded SEO Lead within international product and
engineering teams, partnering directly with development teams, cross-
functional stakeholders, and company leadership to integrate scalable
organic growth systems into product and business strategy.

 Led the design, validation, and end-to-end implementation oversight of
complex SEO growth systems, maintaining hands-on involvement in critical
areas to ensure accuracy, performance quality, and alignment with strategic
intent.

 Managed day-to-day SEO operations across long-term engagements,
including site architecture development, enterprise migrations, continuous
performance monitoring, technical optimization cycles, and iterative search
strategy refinement driven by data analysis and system-level insights.

 Developed and continuously refined frameworks for entity-based SEO,
programmatic SEO, and AI-driven search optimization, including structured
data (JSON-LD), LLM discovery systems, and Google AI Overviews,
building long-term search value and structural adaptability into digital
products.

Senior Technical SEO Consultant / Interim SEO Lead
Independent Consultant (Long-Term Client Engagements) | Full-Time
2019 – 2023

 Engagement model: Long-term embedded engagements supporting
technical strategy, information architecture, and cross-functional SEO
execution.
  Led search strategy, information architecture design, and internal linking
systems within cross-functional product environments.

 Partnered closely with developers, UX designers, and content teams to align
search systems with commercial and conversion objectives.

 Translated complex search engine behavior and audit insights into
actionable technical and content requirements for international digital
platforms.

Technical SEO Specialist
Independent Consultant | Full-Time
2015 – 2019

 Delivered technical SEO execution, comprehensive audits, and organic
growth initiatives across international web properties.

 Built core expertise in crawl optimization, log analysis, site performance,
and search engine retrieval mechanics.

Selected Case Studies & Measurable Results

Case Study: 0 → 2M Monthly Google Clicks (Content SEO Scale)
Large-scale international content platform | Full SEO ownership

The project started as a low-visibility content platform with thousands of
indexed pages but no meaningful organic traction. Through a structured
SEO execution cycle, it was scaled into a high-traffic search asset reaching
2M+ monthly Google clicks and driving direct ad/subscription revenue
growth.
Initial situation:
 No meaningful organic growth over a ~4-month period
 Weak visibility across core informational queries
 No structured SEO or content system
 Poor intent–content alignment and internal linking structure
 No meaningful visibility in AI-driven search surfaces

Operating constraints:
 Highly competitive SERPs with established incumbents
 No existing topical authority and no scalable content infrastructure
 No information architecture designed for AI retrieval and citation
surfaces
 Monetization model directly tied to ad impressions and subscriptions,
requiring massive traffic scale across the entire site layout

Role: Fractional Head of SEO (Full End-to-End Ownership)

Strategic Intent & Core Philosophy:
 Prioritized long-term thematic authority over isolated keyword
targeting. The strategy focused on building systemic content quality
to trigger a compounding topical authority and ranking momentum,
enabling the site to capture top 1–3 positions for high-volume
keywords.
 Positioned content assets around a rigorous intent-mapping
topology. Expanded the information architecture by anchoring
content to verified Wikidata entity identifiers (Q-codes) to ensure
seamless search engine knowledge graph matching and human
engagement.
 Treated AI Overviews and traditional SERPs as a unified system
while actively mitigating zero-click risks. Focused exclusively on
high-depth, holistic topical coverage rather than isolated factual
queries. While AI surfaces synthesize basic answers, driving
 comprehensive value across entire macro-thematic graphs forces
citation clicks and long-term search resilience.

Strategic initiatives:
 Built a search strategy based on intent & SERP gap analysis
 Designed a scalable pillar-and-cluster information architecture to
systematically expand topical authority across high-volume query
groups.
 Built a multi-vector data pipeline extracting programmatic seed data
via Wikipedia API, Google Autocomplete API, and multi-level People
Also Ask (PAA) parsing to map comprehensive user intent and
macro-thematic graphs.
 Developed an automated content quality validation system combined
with manual editorial loops to ensure exhaustive coverage of Google
Knowledge Graph entities and semantic completeness.
 Engineered a semantic internal linking and document structuring
system, improving engagement depth and navigation efficiency,
click-through paths, and visual anchor placements.
 Re-optimized existing pages for intent alignment and SERP
relevance
 Programmatically deployed intent-matched meta titles and
descriptions alongside structured data frameworks to maximize
SERP footprint.
 Collaborated with the development team to optimize crawlability and
infrastructure, mitigating database overhead by deploying a high-
speed, in-memory RAM caching layer (Redis) to process semantic
internal linking and metadata rendering within milliseconds.
 Designed and embedded an AI retrieval layer into the core text
architecture, optimizing Token-to-Fact density and structural layout to
satisfy RAG (Retrieval-Augmented Generation) patterns while
anchoring internal content nodes with Wikidata Q-codes for
advanced entity recognition.
 Implemented continuous SEO iteration system based on
performance data
 Built a custom data pipeline using Python and Pandas to parse SERP
data, calculate AI Overview visibility thresholds, and isolate
incremental traffic gains through automated dataset comparison
 Results:
 Growth from 0 → 2M+ monthly Google clicks (achieving a rapid 1M
to 2M acceleration within a 2-month compounding window)
 3.34M total clicks / 20.6M impressions (GSC) during the 4-month
observation period
 Improved platform-wide Average CTR from 9.8% to 16.2% via
automated intent-matching snippets and rich features
 Accelerated Average Position from 12.0 to 4.7, anchoring the site in
the top 1–3 positions for critical high-volume traffic assets
 Achieved recurring visibility as a cited source in Google AI Overviews
across key business areas, contributing incremental traffic growth
and higher click-through performance

Core impact:
 Transition from fragmented content to system-based SEO
architecture
 Scaling driven by topical authority + intent mapping
 Established AI visibility as a strategic additional acquisition layer,
contributing incremental traffic growth and higher-intent user
acquisition while reinforcing long-term search resilience

Case Study: 1.15M Organic Clicks & 8.55M Impressions Growth
Commercial website | Full-cycle SEO ownership

Managed the end-to-end SEO and product optimization strategy,
transforming an established commercial platform into a high-growth organic
acquisition channel. Led cross-functional collaboration with development,
design, and content teams, generating over 1.15M organic clicks and
reaching a peak of approximately 22K daily organic visits during the
observation period.
Initial situation:

 Flatlined organic growth and limited visibility in a highly competitive
commercial market
 Suboptimal landing page user experience (UX) resulting in lower
conversion rates
 Critical technical SEO bottlenecks, including inefficient crawling,
indexing, and internal linking
 Opportunities to improve alignment between existing landing pages and
transactional search intent
 Emerging opportunities to improve visibility across AI-driven discovery
surfaces for commercial and transactional queries

Operating constraints:
 Limited engineering bandwidth with competing product priorities
 Strong incumbent competitors dominating core SERP segments
 Legacy information architecture limiting both discovery and conversion
flow

Role: Fractional SEO Lead (Cross-Functional Leadership)
Owned SEO strategy end-to-end across technical, content, UX, analytics
and company leadership layers.

Strategic Intent & Core Philosophy:
 Avoided manual page-by-page audits by deploying a programmatic
diagnostic model. Isolated systemic intent-content mismatches across
the catalog layout by flagging negative statistical anomalies in CTR and
Bounce Rate datasets. Resolving execution errors at the cluster level
rather than focusing on isolated pages improved semantic consistency
across the site architecture.
 Addressed engineering bandwidth constraints by rejecting mass-
backlog deployments in favor of an automated Lean-validation
framework. Programmatically calculated business potential vs.
complexity by isolating commercial clusters at the validation threshold
(hovering around the top 10 positions). Running low-cost micro-
experiments on these high-potential striking-distance nodes filtered out
low-efficiency segments, allowing the team to concentrate resources
strictly on areas demonstrating the fastest and most manageable
scalability.
 Positioned technical SEO and product design as a unified conversion
vehicle. Collaborated directly with core product layers to optimize
checkout flows, product quality transparency, and fulfillment clarity,
recognizing that user-centric commerce metrics are critical components
used by search engines to evaluate platform authority.
 Structured landing page layouts to preemptively address secondary
customer friction points through data-driven interactive features,
explicitly aligning the platform with Google's Helpful Content System and
Quality Raters Guidelines.
 Approached traditional organic visibility and generative AI-driven answer
engines as a connected high-conversion ecosystem. Recognizing that
AI engines synthesize comparison grids for transactional queries, the
strategy focused on transforming product attributes into machine-
extractable features. Capturing these recommendation citations was
treated as a primary driver for Bottom-of-Funnel user acquisition with
exceptionally high conversion potential.

Strategic initiatives:
 Built an automated analysis pipeline combining intent mapping and
SERP gap analytics to systematically extract high-yield product
matrices.
 Programmatically isolated clusters at the striking distance threshold
(average positions 8–12) to pinpoint segments primed for immediate
traffic acceleration
 Designed and executed low-fidelity SEO micro-experiments across
threshold layouts to validate search signals and conversion viability
before committing engineering sprints
 Eliminated lower-performing alternative segments based on empirical
data, focusing development resources exclusively on high-conversion
transactional clusters
 Partnered with UI/UX designers to overhaul landing page architecture
using a low-risk experimental framework; introduced high-utility
conversion elements like programmatic comparison matrices and
 structured intuitive layers that preemptively addressed secondary
customer friction points, delivering a clean UX for users while providing
a high-density data layer of commercial entities for search engines and
AI discovery systems
 Engineered an automated script to continuously aggregate behavioral
metrics (CTR, Bounce Rate), isolating statistically underperforming
segments to reveal latent intent-content friction points
 Applied programmatic clustering algorithms to categorize low-
performing assets, systematically prioritizing these node blocks for
manual expert evaluation and targeted intent-alignment overrides
 Directed the engineering team to eliminate technical crawling and
indexation bottlenecks while restructuring site taxonomy
 Improved landing page structure to better match transactional search
intent and reduce friction between query → landing → conversion
 Designed and deployed a commercial GEO framework to capture high-
conversion AI comparison widgets; implemented advanced Schema.org
validation (Product, Offer, MerchantReturnPolicy, ShippingDetails)
combined with structured, high-density product data sections.
Programmatically embedded explicit lists of product parameters,
features, limitations, advantages, and disadvantages, enabling RAG-
driven discovery systems to effortlessly interpret, compare, and cite our
platform as a primary merchant choice.
 Implemented an analytics-driven performance monitoring framework to
sustain long-term rankings and execute iterative on-page updates

Results:
 Generated 1.15M+ organic clicks and 8.55M impressions during the
optimization period
 Organic traffic scaled from ~4K to ~22K daily peak (~5x growth)
 Achieved an Average CTR of 13.5% and an Average Position of 5.6
across the entire optimization period (improving from legacy baselines
of 9.2% CTR and 11.0 Position), competing against paid ads by
deploying advanced Rich Snippets (Review and FAQ Schema) and
achieving recurring visibility within Google AI Overview comparison
experiences
 Significantly improved organic conversion performance, driving
measurable increases in Conversion Rate (CR) from organic visit to cart
addition/form submission, while directly contributing to overall order
volume and annualized revenue growth through cross-functional product
alignment
  Established recurring visibility across AI-generated answer surfaces,
contributing additional high-intent traffic acquisition and improved search
resilience

Core impact:
 Established a scalable SEO operating framework that aligned technical
architecture, content strategy, and conversion optimization, enabling
sustainable organic growth and long-term business performance.

Case Study: Complex E-commerce Migration & Post-Launch
Organic Growth
E-commerce Platform | Full-Cycle SEO Ownership

Managed the end-to-end SEO migration strategy and information
architecture redesign for a revenue-critical e-commerce platform. Led cross-
functional collaboration with engineering and design teams to ensure zero
critical organic traffic loss during the transition, successfully executing a
post-migration optimization cycle reaching a stabilized ~3.52M organic
clicks, 37.5M impressions, and a post-migration peak of ~34K daily organic
visits (vs. ~20K pre-migration baseline).

Initial situation:
 Legacy site architecture and tech stack heavily constraining organic
scalability.
 Risk of severe traffic and revenue drops during a necessary full-site
platform migration.
 Inefficient internal linking taxonomy and crawl inefficiencies across
faceted navigation.
  Outdated URL structures and technical debt requiring a complete
overhaul.

Operating constraints:
 Legacy platform with substantial technical debt.
 Revenue-critical migration with limited tolerance for traffic loss.
 Strong dependencies between commercial clusters requiring controlled
sequencing of structural changes.

Role: Embedded SEO Lead
(Migration Strategy & Cross-Functional Leadership)

Strategic Intent & Core Philosophy:
 The migration was structured around maintaining continuity of entity
relationships across a rapidly evolving e-commerce architecture, where
changes to information architecture could directly affect search engine
interpretation of high-value commercial clusters.
 The migration followed a low-risk, high-control framework based on a
phased, cluster-based rollout. By isolating anomalies in non-critical
sections and compounding tactical optimization wins at each phase, this
iterative approach embedded incremental search-signal enhancements
to create momentum, ensuring high-revenue categories migrated
directly into a pre-optimized environment for accelerated recovery and
growth.
 Scalability considerations were embedded into the design of the new
structure to support future catalog expansion, with attention to
maintaining consistent crawl patterns and avoiding fragmentation in
indexation across dynamic sections of the site.
 Near-real-time visibility into crawling and indexation behavior provided
continuous validation of search engine interpretation throughout the
transition while preventing uncontrolled propagation of structural
inconsistencies.

Strategic initiatives:
 Designed a full pre- and post-migration SEO framework including crawl
simulation logic, baseline SEO signal snapshot, and cluster grouping
based on business criticality and SEO dependency.
 Defined URL mapping, redirect logic (optimized via Redis in-memory
cache), and canonical frameworks to preserve ranking equity across all
key commercial segments.
  Implemented a validation gate system to enforce consistency between
expected and actual search engine behavior during each migration
phase
 Executed automated structural Diff-analysis (via Python/Pandas) to
verify each migration phase against the pre-migration snapshot,
simulating updated crawl paths and internal linking structures to detect
semantic deviations before full exposure
 Coordinated engineering efforts to eliminate legacy crawl inefficiencies
while restructuring indexation behavior throughout the transition
 Collaborated with product teams to redesign e-commerce taxonomy,
improving faceted navigation architecture and reducing generation of
duplicate or low-value pages.
 Monitored real-time post-migration server logs via ELK Stack (Kibana)
to isolate algorithmic fluctuations, instantly resolve 500/404 server
errors, and guide semantic signal stabilization across key commercial
clusters.

Results:
 Maintained stable organic visibility throughout full-site migration with no
critical loss in core revenue-driving segments
 Achieved 3.52M+ organic clicks and 37.5M impressions during the
migration observation period (stabilized baseline performance)
 Maintained an Average CTR of 9.4% (up from 9.1% baseline) and
accelerated Average Position to 13.3 (from 16.3 legacy baseline) during
full-site re-indexing
 Achieved a steady upward post-migration trajectory, outperforming
legacy site traffic and visibility peaks

Core impact:
 Converted a high-risk platform migration into a controlled search system
transition, preserving revenue-critical organic visibility while establishing
a scalable information architecture capable of supporting future catalog
growth.