His work sits between SEO, analytics, automation, and product thinking: taking messy marketing operations and turning them into structured systems that are easier to trust, scale, and maintain.
SEO
SEO was the first major chapter - roughly 2 to 3 years of hands-on work across audits, local SEO, content, reporting, and client communication. The work was practical: finding problems, prioritizing fixes, explaining results clearly, and tying SEO efforts back to business outcomes.
This stage built the foundation for everything that came later.
- Technical SEO audits and implementation planning
- Local SEO and on-page optimization
- Keyword research and search intent analysis
- Google Search Console analysis and performance monitoring
- Client reporting, review calls, and communication
Data Reporting & Analytics
As SEO work scaled, reporting became a core part of the process. The focus moved beyond rankings into attribution, tracking accuracy, and explaining performance through data.
This is where inconsistencies between platforms, missing data, and broken attribution became impossible to ignore.
- GA4 and Google Tag Manager, including client-side and server-side setups
- Event tracking architecture and attribution logic
- Looker Studio dashboard design for clients and agency reporting
- BigQuery querying, transformations, and data modeling
- Debugging tracking gaps and data inconsistencies
Data Pipelines & ETL
With more clients and more data sources, manual work became the bottleneck. Small scripts gradually turned into structured workflows for moving, cleaning, and shaping data across tools.
This is where ETL thinking became central: extract, transform, load, applied to marketing data.
- Designing ETL flows for marketing and reporting data
- Connecting multiple APIs and data sources
- Cleaning, normalization, and structuring datasets
- Automating data movement between tools and reporting layers
- Building repeatable workflows instead of one-off exports
Development & Automation
As the workflows became more complex, scripts were no longer enough. That led to building internal tools, backend logic, and automation systems that could support recurring work in a more durable way.
Development shifted from supporting operations to shaping how they run.
- Node.js backend logic, APIs, and internal services
- Workflow automation and custom pipelines
- Web scraping and data extraction with tools like Puppeteer
- Internal tools for audits, reporting systems, and data operations
- Reusable application logic around publishing, reporting, and automation
Systems, Product Thinking & Product Management
A big part of Igor's systems thinking came from real product ownership inside a large internal business platform. That work has included managing developers, shaping product direction, structuring features, and solving operational problems in a live environment where the system needs to work for actual teams and real workflows.
This is where product management became part of the skill set: turning messy business needs into workable specs, deciding what matters most, structuring systems so they scale, and taking responsibility for a large share of what gets built.
- Product management across complex internal systems
- Managing developers and coordinating implementation
- Designing scalable systems for multiple teams, workflows, and clients
- Structuring relationships between data sources, pipelines, reports, and operations
- Building internal dashboards, operational tools, and reusable system foundations
- Solving real-world product and systems problems under practical constraints
Engineering (Exploration)
Alongside the applied work, Igor has also been exploring lower-level engineering concepts through C++ fundamentals and deeper systems thinking.
The goal here is not theory for its own sake - it is understanding performance, memory, and execution flow more clearly, then applying that thinking back into automation and product work.
- C++ fundamentals and lower-level system thinking
- Understanding performance, memory, and execution flow
- Applying engineering principles to data and automation problems
Intelsquid
Intelsquid came out of that progression.
What started as separate scripts for SEO clients evolved into pipelines, then into small applications, and eventually into a more unified system for handling marketing data.
Today, Intelsquid has a twofold purpose:
- To remain a tool Igor uses himself in real day-to-day work, while also being useful to others
- To serve as a place to share resources, knowledge, blog posts, scripts, tools, and practical materials that other marketers may find useful
The ongoing goal is continuous on both sides:
- Keep expanding the app's functionality and supported app library
- Keep sharing more expertise, workflows, and useful resources over time