Data Platform Reliability Engineer

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)