Senior Data Engineer

Are you interested in how data comes to life? We're looking for someone who enjoys building things from scratch and seeing their work make a real difference for students, teachers, and professionals. If you like the idea of working with a friendly team to build clean, helpful data tools, we'd love to chat with you! Then Kahoot! has the job for you! We're looking for a Senior Data Engineer to join the global Kahoot! Data Team and help us build the next generation of the Kahoot! data platform. And we mean the next generation quite literally. We are building an agent-driven data warehouse. You'll be a senior voice in a small, high-trust team that owns everything from ingestion to the semantic layer. That means real ownership, and you work directly with the analysts and business stakeholders who depend on your data. It also means real leverage — the models you build are the ones the whole company reports on. The team The Kahoot! Data Team is a small, cross-functional group of seven covering both data engineering and analytics. Our engineers own the core data platform and our automation and data product work; our analysts are embedded with Sales, Product, and Finance, so the people consuming your models sit in the same standup as you. We build and run the Data Kabal — the Kahoot! data platform. Concretely, that is: Google BigQuery as our data warehouse Dagster+ as our orchestrator, coordinating both dbt models and Python assets Terraform / Terragrunt for infrastructure as code, stored on GitHub AI agents (Claude, MCP servers, coding and data agents) as a first-class part of the platform rather than a side experiment We care a lot about conventions and documentation as code: Identifiers and naming conventions come from central registries, data models have data quality checks, and data documentation lives in the repository. Hence, it propagates downstream instead of going stale in a wiki. That discipline is what makes the agent work possible. Agent-driven data warehousing This is where we are placing our biggest bet, and where this role will have the most impact. We are building data agents across three areas: Modeling — agents that scaffold and refactor dbt models, Dagster assets and tests in line with our conventions, propose dimensional models from source schemas, and handle the mechanical parts of migrations and remodeling so engineers can spend their time on design Monitoring — agents that watch asset checks, freshness, and pipeline failures, correlate them with recent changes, do the first round of root cause investigation, and hand a human a diagnosis instead of an alert Analytics — agents that answer business questions against our governed models and semantic layer, with the lineage and caveats attached, so stakeholders get trustworthy self-service and analysts get their time back The company's HQ is in Oslo, Norway, and Oslo is our first choice for this position. This role reports to the Chief Technology Officer, located in Norway. Responsibilities Own data domains end to end — from source system and ingestion, through dbt models in BigQuery, to the marts our analysts and BI tools build on Design and build pipelines in Dagster+, orchestrating both dbt models and Python assets, with partitions, sensors, and automation conditions that fit the data rather than a cron guess Model our data warehouse using dimensional modeling, and take a leading role in remodeling domains that have outgrown their original design Set and hold the quality bar — data quality and freshness checks, meaningful tests, and monitoring and alerting so we find problems before our stakeholders do Build and operate our data agents for modeling, monitoring, and analytics — design the workflows, tools, and guardrails, decide where an agent acts autonomously and where a human approves, and measure whether the output is actually good Make the warehouse agent ready — treat metadata, documentation, data contracts and the semantic layer as production interfaces, because they are what agents (and people) reason from Build data products and automations that put data to work directly in the business, not only in dashboards Modernize and consolidate the platform — we are actively migrating legacy workloads onto Dagster and dbt, and paying down modeling debt as we go Own your infrastructure as code with Terraform/Terragrunt, and treat DataOps (CI/CD, environments, branch deployments) as part of the job Ensure compliance with data governance — high quality, secure, well documented and correctly retained datasets, including data deletion and privacy requirements Work directly with stakeholders in Sales, Product, Finance and Engineering: turn a vague business question into a well-specified data model, and say no to the ones that should not be built Raise the level of the team — thorough rubber ducking, code reviews, strengthening our conventions, writing the documentation that makes the platform learnable, and mentoring colleagues and new joiners Requirements We're looking for someone who has: A degree in a computing-related field, or equivalent practical experience 5+ years of experience as a data engineer, including clear ownership of production data pipelines and warehouse models Strong SQL — you are comfortable reasoning about query cost and performance, not just correctness Strong Python, with an eye for clean, tested, typed, and reviewable code Solid experience with a cloud data warehouse — BigQuery ideally, otherwise Snowflake, Redshift, or Databricks Hands-on production experience with an orchestration framework (Dagster, Airflow, Prefect, Argo...) Hands-on experience with dbt, or a comparable transformation framework Experience with dimensional data modeling (Kimball/star schema) and an opinion about when to apply it Experience integrating third-party APIs and source systems into a warehouse, including the unglamorous parts: pagination, backfills, schema drift, and late-arriving data Comfortable with cloud pla