Data Platform Engineer

XpertDirect; København; Hybrid; DKK 750,000 - 950,000; Full time

Job summary

Energy Technology seeks a Data Platform Engineer in Copenhagen on a hybrid basis to build and evolve a shared data platform for analytics, operational apps, and ML workloads.

You will work across data engineering, distributed processing, streaming, cloud infrastructure, and platform engineering to enable data at scale for teams across the organisation.

Qualifications

• 4+ years in Data Engineering or Data Platform roles.
• Experience building and operating production data systems.
• Experience with real-time data processing and streaming.
• Strong knowledge of cloud infrastructure (AWS).

Responsibilities

• Build and evolve the shared data platform for analytics, operational apps, and ML workloads.
• Collaborate across data engineering, distributed processing, streaming, cloud infra and platform engineering.

Skills

Python

Apache Spark

Kafka

Airflow

Snowflake

Kubernetes

AWS

Tools

Terraform

dbt

Kafka Connect

Databricks

Docker

Prometheus

OpenTelemetry

Job description

Data Platform Engineer

Copenhagen, Denmark — Hybrid

Energy Technology | Data Platform | Data Engineering | Cloud Infrastructure | Real-Time Data

Our client, a growing Energy Technology company based in Copenhagen, is looking for a Data Platform Engineer to build and evolve the shared data platform supporting analytics, operational applications, and machine-learning workloads.

You'll work across data engineering, distributed processing, streaming, cloud infrastructure, and platform engineering, creating the foundations that allow teams across the organisation to work effectively with data at scale.

Core Skills

• 4+ years in Data Engineering, Data Platform Engineering, Data Infrastructure, or a similar role
• Python
• Apache Spark
• Apache Kafka
• Apache Airflow
• Snowflake
• Kubernetes
• AWS
• Experience building and operating production data systems

Nice to Have

• Terraform
• dbt
• Kafka Streams / Kafka Connect
• Databricks
• Docker
• Prometheus / OpenTelemetry
• Data-quality and observability tooling
• MLflow
• CI/CD / GitOps
• Data lake / lakehouse architectures
• Infrastructure as Code
• Experience supporting ML or real-time operational workloads