Lead Data Platform Engineer for AI & Analytics

Planday; København; On-site; DKK 900,000 - 1,200,000; Full time

Benefits offered by this job

Pension

Health insurance

Employee Share Plan

Growth opportunities

Flexible remote work

Social culture

Meaningful work

Healthy work-life balance

Job summary

Planday is seeking a Lead Engineer- Data to define the data platform architecture that supports analytics, AI/ML, and trusted data for the business. You will work with Data, ML, Platform, and Product Engineering to ensure scalable ingestion, modelling, and orchestration across the stack.

The role requires deep Snowflake/dbt expertise, cloud and Kubernetes skills, and strong SQL/Python. Flexible remote work and a collaborative, output-driven culture are offered.

Qualifications

• Snowflake and dbt experience with modelling and semantic layers.
• Production pipelines with Dagster or similar tools and observability.
• Cloud infrastructure and Kubernetes experience with IaC (Terraform).
• Strong SQL and Python for data pipelines and automation.
• Software engineering discipline across CI/CD, testing, and code review.
• Ability to translate trade-offs into clear narratives for stakeholders.

Responsibilities

• Set the architectural direction of the data platform for analytics and AI/ML.
• Design dbt models, semantic layer, and quality standards with lineage.
• Own Dagster orchestration and onboarding of new sources.
• Advance self-serve analytics and semantic models for BI.
• Build scalable, low-latency foundations for real-time use cases.
• Maintain reliability, performance, cost-effectiveness, and compliance.

Skills

Data platform architecture

SQL & Python programming

CI/CD & testing

Stakeholder communication

Cost & performance optimization

Tools

Dagster

Airflow

Prefect

Snowflake

dbt

Azure

AWS

GCP

Kubernetes

Terraform

Job description

Planday is seeking a Lead Engineer- Data to define the data platform architecture that supports analytics, AI/ML, and trusted data for the business. You will work with Data, ML, Platform, and Product Engineering to ensure scalable ingestion, modelling, and orchestration across the stack.

The role requires deep Snowflake/dbt expertise, cloud and Kubernetes skills, and strong SQL/Python. Flexible remote work and a collaborative, output-driven culture are offered.