Data Platform Engineer: Scalable Pipelines

Axis Communications; Lunds kommun; On-site; SEK 900,000 - 1,100,000; Full time

Benefits offered by this job

Bonus

Fridays cake

Wellness allowance

Health insurance

Axis bicycle

Job summary

Axis Communications in Lund is seeking a Data Platform Engineer to build and operate data pipelines across cloud and on‑prem environments. You will own infrastructure, databases, and visualization tools like ClickHouse, Superset, and Kubernetes.

The role emphasizes clean Python code, testing, and CI/CD, with deployment to staging and production. We value collaboration, curiosity, and a practical approach to scaling data platforms, including on‑premises architecture as data volumes grow.

Qualifications

• Experience in software and data engineering, with strong Python, Linux, and Git skills.
• Knowledge of PostgreSQL and ClickHouse databases.
• Experience with Kubernetes and BI/visualization tools is a plus.

Responsibilities

• Build and operate data pipelines to collect, process, and store data in cloud and on‑prem environments.
• Own infrastructure, databases, and visualization tools (ClickHouse, Superset, Kubernetes).
• Develop clean, maintainable Python code on Linux in a CI/CD setup with Git, tests, and linters.
• Deploy to staging and production; manage automated tests and quality checks.
• Strengthen and evolve on‑premises architecture as data volumes grow and reliability needs increase.

Skills

Python

Linux

Git

PostgreSQL

ClickHouse

Kubernetes

English proficiency

Education

Degree in Computer, Electrical, or Physics Engineering

Tools

Dagster

Apache Superset

DataHub

Job description

Axis Communications in Lund is seeking a Data Platform Engineer to build and operate data pipelines across cloud and on‑prem environments. You will own infrastructure, databases, and visualization tools like ClickHouse, Superset, and Kubernetes.

The role emphasizes clean Python code, testing, and CI/CD, with deployment to staging and production. We value collaboration, curiosity, and a practical approach to scaling data platforms, including on‑premises architecture as data volumes grow.