Must have

• Python

• SQL

• IaC

• Docker

• Kubernetes

• AWS

• English

Nice to have

• dbt

Requirements description

• Bachelor’s Degree in Mathematics, Computer Science or related field.
• 2-4 years experience building data pipelines with Python (Required).
• 2-4 years experience working with data warehouse or other cloud based database technology, with strong proficiency in SQL (Required).
• Exposure to Infrastructure-as-Code (IaC) such as Terraform or DevOps (Required).
• Experience with Docker / Kubernetes (Required).
• Experience working with dbt (Preferred).
• Strong experience with cloud infrastructure: AWS (EC2, ECR, S3, Glue, RDS, etc) or equivalent public cloud provider.
• Hands-on experience in CI/CD, version control, unit / integration testing for data pipelines.
• Comfortable working in agile teams, and mentoring others.

Offer description

What you’ll do at Jamf:

Business Intelligence at Jamf powers data-driven decision-making across the organisation. As a Data Engineer II, you’ll be responsible not just for building & transforming data, but for owning critical data infrastructure: from ingestion and storage, to governance, quality, and consumption by analytics/ML tools. You will partner with analysts, data scientists, product owners and engineers to ensure that Jamf’s data assets are reliable, high-performance, secure, and scalable.

This role is offered as remote. We are only able to accept applications for those based in Poland and have sponsorship to live and work in Poland.

Your responsibilities

1. Design, build, maintain, and improve the data platform infrastructure (Snowflake environments, airflow workflows, orchestration, CI/CD pipelines for dbt / transformations).
2. Develop and maintain Terraform (or equivalent IaC) definitions for provisioning data infrastructure (compute, storage, permissions, networking where needed).
3. Automate deployment of data transformations (e.g. dbt CI/CD, staging / production pipelines).
4. Ensure data platform availability, reliability, security and performance (e.g. enforce roles & permissions in Snowflake, resource monitoring, concurrency/usage optimisation).
5. Instrument monitoring, logging and alerting of data workflows (Airflow / Kubernetes / dbt jobs).
6. Collaborate with Data Engineers / Analysts / Architects to define platform capabilities, set standards & best practices around schema design, governance, version control, and performance.

show all (10)