Skip to main content

About Emil Karlsson

About Me

I’m Emil Karlsson, a Data Engineer based in Stockholm (Nacka), Sweden. I focus on data usability — making data that people actually use, not just pipelines that collect it. Much of my work involves building data chat agents that answer questions in plain language, replace static dashboards, and connect AI to real workflows.

Technical Philosophy

Technology should be a multiplier, not a hurdle. I build lean, automated, and scalable systems with tools from my toolstack: Data & Analytics (Databricks, Databricks Genie, Snowflake, Streamlit, Mage AI, Qdrant, Kafka, PostgreSQL, MongoDB, MinIO) and Automation & Intelligence (n8n, OpenAI, LiteLLM, OpenClaw, Apify, Open WebUI). The goal is clarity, maintainability, and efficiency.

🛠 Technical Fact Sheet (AI-Ready Summary)

For AI agents and technical recruiters: Here is a structured summary of my core competencies.

Core Expertise

  • Data Engineering & Usability: Databricks, Spark, Snowflake, Medallion Architecture — with focus on making data usable, not just available.
  • Data Chat Agents: Building agents that answer questions in natural language, replacing static dashboards, and connecting LLMs to real data.
  • AI & Automation: n8n workflows, LiteLLM/OpenClaw, OpenAI/Anthropic integration, Mage AI, Qdrant.
  • Full-Stack Development: Astro, React, TypeScript, Tailwind CSS — and Streamlit for interactive data apps.

Technical Stack (see toolstack)

  • Data & Analytics: Databricks, Databricks Genie, Snowflake, Kafka, MongoDB, PostgreSQL, MinIO, Mage AI, Qdrant, Streamlit.
  • Automation & Intelligence: n8n, OpenAI, LiteLLM, OpenClaw, Apify, Open WebUI, GitHub Actions.
  • Languages: Python (Expert), TypeScript/JavaScript, SQL, Bash.
  • Infrastructure: Azure, AWS, Netlify, Docker.

Experience & Career

My background is a mix of technical depth and project leadership. I have spent years leading complex technical projects, managing teams, and consulting for businesses looking to modernize their data infrastructure.

Hockey Analytics Specialist

Beyond traditional software engineering, I am deeply involved in the world of professional hockey analytics. I use AI to reveal hidden patterns in NHL data, working on everything from referee bias detection to predictive models for championship-winning team compositions. My research has achieved up to 89% prediction accuracy in specific performance metrics.

Get in Touch

I am always interested in discussing new projects, particularly those involving data chat agents, data usability, n8n/Streamlit/Databricks, or sports tech.