Senior Data Platform Reliability Engineer (Kubernetes)
Summary
Operates and improves the company's data platforms on Kubernetes (on-prem and AWS EKS/GCP GKE): deploying changes via GitOps, monitoring health and observability, joining 24x7 incident rotations, troubleshooting user issues, and mentoring junior engineers at a technical consulting firm serving high-tech clients.
A technical consulting company specializing in operational services for the high-tech industry. We help platform and infrastructure teams operate multi-cloud environments, execute complex migrations, and enable seamless app deployments.
Your role:
As a Senior Data Platform Engineer, you will be responsible for operating, maintaining, and continuously improving the company’s data platforms running on Kubernetes (On-premise and/or on AWS/GCP) - similar on the DoEKS (Data on EKS) / AIoEKS (AI on EKS) deployment frameworks
- Deploy new releases and configuration changes through GitOps/DevOps
- Monitor platform and service health using logs, metrics, and observability tools
- Participate in incident response, root cause analysis and 24x7 operational rotations
- Improve platform observability, operational tooling/automations, self-service capabilities and reliability practices to reduce recurring issues
- Investigate & troubleshoot user concerns by either correlating them to system-related issues, breaking integrations and/or user-specific errors/misconfigurations up to recommending/executing resolutions
- Provide technical mentorship to junior engineers
- Advocate for platform standards, security best practices, and operational excellence
Your Qualifications
- 3+ years of solid experience supporting production data workloads/platforms (Spark/Airflow/Jupyter)
- 5+ years of hands-on experience on ETL/ELT pipeline development & data transformations (Python/Java & SQL)
- Practical proficiency in Kubernetes environments including Cloud-provider managed Kubernetes flavors (AWS-EKS/GCP-GKE)
- Comprehensive knowledge on Linux environments, microservice architectures and service communication patterns
- Strong troubleshooting fundamentals such as application crashes, resource contentions, service latency, and scaling behavior
- Well-rounded competency in analyzing logs, metrics, monitoring systems, and service KPIs
Plus points if you have:
- Exposure in other Data/AI platforms such as Flink, Trino, Druid and Ray.I
- Hands-on experience with automation or scripting (Bash, Python)
- Kubernetes or Data certifications (CKAD, AWS Certified Data Engineer)