Frequently Asked Questions
Answers to common questions about my work, approach, and how to collaborate. For more detail on specific topics, explore the topic hubs or blog.
This site is where I write about data engineering, automation, and the occasional deep dive into hockey analytics. Over time, certain questions come up repeatedly—about how I approach problems, what tools I use, or why I write about particular topics.
The answers below reflect how I think about these topics now. They'll likely evolve as I learn more. If something isn't covered here, the rest of the site—especially the articles and projects—probably addresses it in more depth.
I've organised the FAQ into four sections: who I am and what this site is for, how I think about data engineering and automation, what kinds of projects I take on, and practical details about working together. Each answer includes links to relevant articles or topic pages for deeper exploration.
About the Site & Direction
Who is Emil Karlsson?
I'm a data engineer and full-stack developer based in Stockholm, Sweden. This site is where I think through problems publicly—exploring how data engineering, AI automation, and modern web development intersect in practice. My work spans building modern data stacks for clients, workflow automation systems, and occasionally diving deep into hockey analytics when questions about patterns in NHL data won't leave me alone.
What is this site about?
This site serves as both a portfolio and a thinking space. I write about projects I've built, technical approaches I've explored, and systems I find interesting. The focus tends to be on practical implementation—how things actually work, not how they're supposed to work. If you're curious about the topics I cover, the Topics page organizes everything by area.
How I Think and Work
What is your approach to data engineering?
I focus on building systems that solve real problems without unnecessary complexity. This often means choosing the right tools for the context—sometimes that's Databricks and medallion architecture, sometimes it's a simpler stack. The modern data stack pillar covers much of this thinking in detail. I also write about cost-effective approaches and automation patterns that help teams move faster.
What technical stack do you use?
For data engineering: Python, Databricks, Snowflake, SQL, and cloud platforms (Azure, AWS). For automation and orchestration: n8n for workflows, various LLM APIs when appropriate. For web development: Astro, React, and TypeScript. The toolstack page lists everything I actively use, organized by category.
What is your expertise in hockey analytics?
I use AI and machine learning to analyze NHL data, looking for patterns that aren't immediately obvious. Some of this work has involved predicting championship outcomes, analyzing referee bias patterns, and optimizing equipment physics. The hockey analytics section has the deeper dives. I'm particularly interested in how Swedish NHL players perform and what data reveals about their game.
About the Projects
What kind of projects do you take on?
I work on projects where data engineering, automation, or modern web development are central. This includes building AI agents that replace traditional workflows, automating content pipelines, creating conversational analytics prototypes, and designing cost-effective data stacks. The projects section shows examples of completed work.
Do you work with AI automation?
Yes. I build workflow automation systems using n8n and AI agents that integrate LLMs into practical workflows. This might involve building custom AI agents for websites, creating proactive systems that suggest actions, or automating business processes. The work tends to be practical—systems that solve actual problems rather than demos.
Practical Questions
How can I work with you?
You can reach out through the contact form or connect via LinkedIn. I prefer starting with a conversation about what you're trying to solve rather than jumping straight into scoping. This helps ensure we're aligned on the problem before discussing solutions.
Are you available for remote work?
Yes. I work with clients globally and am based in Stockholm (Nacka), Sweden. Remote collaboration is the default, though I can work on-site when it makes sense for the project.
What should I read first?
That depends on what you're curious about. If you're interested in workflow automation, start with the n8n vs Zapier comparison. For AI agents, the guide to building an AI agent covers the full stack. For hockey analytics, the referee bias analysis is a good entry point. The Topics page organizes everything if you want to explore by theme.
Do you work with startups or only enterprises?
Both. I've worked with early-stage startups needing lean, cost-effective data solutions—often combining n8n, cloud data warehouses, and open-source tooling—and with larger organisations with established stacks. The common thread is projects where data, automation, or modern web development are central. Startups often benefit from my experience in building quickly without over-engineering; enterprises benefit from architecture reviews and integration work.
What formats do you offer for collaboration?
Engagements range from one-off consultations (e.g. a technical review or architecture workshop) to fixed-scope pilots and ongoing retainer work. I also do project-based work for discrete deliverables—for example, building an AI agent, setting up a data pipeline, or building a web application. The contact page is the best place to describe your situation; we can then decide which format fits.
Have a specific question?
If something isn't addressed here, feel free to reach out. I'm always open to discussing technical challenges, new projects, or ideas worth exploring.
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