DevOps Engineer Data & Platform Operations

Siemens AG
Erlangen, Germany
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English, German

Job location

Erlangen, Germany

Tech stack

Artificial Intelligence
Amazon Web Services (AWS)
Cloud Engineering
Databases
DevOps
Python
PostgreSQL
Message Broker
Redis
Prometheus
Software Deployment
Data Streaming
Grafana
FastAPI
Kubernetes
Information Technology
Rancher
Kafka
Machine Learning Operations
Data Pipelines
Dynatrace

Job description

  • You implement and maintain GitOps workflows using ArgoCD and Kargo for platform and application deployments across multi-cluster environments managed with EKS, Rancher and vCluster.
  • You support product teams in adopting platform standards and deployment best practices and build self-service capabilities around it.
  • You contribute to AIOps initiatives, leveraging AI-driven approaches for platform operations and automated remediation.
  • You collaborate closely with DevOps engineers and development teams to continuously improve the platform ecosystem.
  • Finally, you contribute to platform observability, monitoring and operational automation.

Requirements

Do you have experience in Redis?, Do you have a Bachelor's degree?, * You hold a Bachelor's degree in Computer Science, Information Technology, or a comparable qualification.

  • You bring several years of experience in Platform Engineering, DevOps, or Cloud Engineering roles with a strong focus on data-intensive systems.
  • You have proven deep hands-on expertise with Kubernetes, including running stateful workloads (databases, message brokers) in production. Additionally, you are familiar with multi-cluster Kubernetes management using tools such as vCluster, Rancher and delivery frameworks like KRO or KubeVela.
  • You have hands-on experience with GitOps and deployment tooling, specifically ArgoCD & Helm.
  • You have experience with Kafka and streaming applications and managing data pipelines. You should have experience in maintaining productive Kafka clusters, preferably on Kubernetes.
  • You are experienced with cloud platforms (particularly AWS) and Python for automation and tooling. FastAPI knowledge is preferable.
  • You have worked with PostgreSQL and Dragonfly/Redis in production environments, including operations, tuning, and high-availability configurations.
  • You bring experience or strong interest in AI/ML Ops and you are familiar with observability tools like Prometheus, Grafana or Dynatrace.
  • You are fluent in English; German skills are a plus.

Benefits & conditions

4.04.0 out of 5 stars Erlangen Hybrid work Permanent, Full-time

About the company

Siemens Mobility is a separately managed company of Siemens AG and has been a leading supplier in the field of mobility for over 160 years. Our core business includes rail vehicles, rail automation and electrification solutions, turnkey systems and related services. We have always been very innovative in making traveling faster, safer and more comfortable. Today, we need new solutions to new challenges such as climate change and rising populations worldwide. That's what drives us. That's why we shape mobility with passion, always being one step ahead. Through digitalization, we make infrastructures smart and create opportunities that get us from A to B sustainably and seamlessly. Our over 40,000 employees are pioneers in mobility who help to keep the world moving. What do you contribute? Railigent X is Siemens Mobility's open IoT platform leveraging AI and big data to transform rail asset management. As part of the Ecosystem-Enabler team, you will focus on platform infrastructure, automation and developer enablement. You operate at the intersection of platform engineering and data, enabling teams across the ecosystem to reliably produce and consume data at scale. * Your responsibilities include designing and operating Kubernetes-based platform infrastructure for both stateless and stateful workloads, including databases and streaming systems.

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