AI Engineer

Y-Axis; Kolding; Hybrid; DKK 650,000 - 900,000; Full time

Job summary

Y-Axis is seeking a Data Engineer to join our analytics team in Denmark. You will design and maintain data pipelines, ensuring robust ingestion from APIs, servers, or files into raw and staging tables.

You will build and maintain dbt models, implement Airflow-based orchestration, and champion data quality with automated checks. This role offers growth into broader platform responsibilities while delivering reliable data tooling.

Responsibilities

• Build and maintain robust data ingestion scripts from APIs, servers, or files into raw and staging tables.
• Grow analytics-ready data layer by building and maintaining clean staging and mart models with clear naming, documentation, and automated tests.
• Extend and troubleshoot workflow orchestration frameworks (Airflow) by adding tasks, fixing runs, and managing dependencies, scheduling, and retry behavior.
• Own pipeline health by adding and monitoring data quality checks (freshness, volume anomalies, null/duplicate validations) and investigating data discrepancies flagged by stakeholders.
• Write clean code, maintain READMEs, and leverage AI-assisted tools to speed up drafting while verifying correctness before pushing to production.
• Build a solid track record in data modelling, testing, and pipeline reliability as a foundation for growing into orchestration design and platform topics.

Job description

• uild Reliable Ingestions: Build and maintain robust data ingestion scripts to pull data from APIs, servers, or files into raw/staging tables. You ensure correct handling of retries, incremental/idempotent loads, and graceful error management.
• Model with dbt: Grow our analytics-ready data layer by building and maintaining clean staging and mart models with clear naming, documentation, and automated tests, focusing on simplicity and readability before optimisation.
• Support Orchestration: Extend and troubleshoot our existing workflow orchestration frameworks (Airflow). You will add tasks to existing DAGs, fix failing runs, and grow into mastering dependency, scheduling, and retry behaviour.
• Champion Data Quality: Own pipeline and model health by adding and monitoring data quality checks (freshness, volume anomalies, null/duplicate validations) and investigating data discrepancies flagged by different stakeholders.
• Document & Accelerate: Write clean code, maintain useful READMEs, and leverage AI-assisted development tools to speed up drafting while taking full ownership of verifying correctness before pushing to production.
• Grow your ownership: Build a solid track record with data modelling, testing, and pipeline reliability as a foundation for growing into orchestration design, platform topics, and broader engineering responsibilities over time.