Senior Data Platform Reliability Engineer

About the role As a Senior Data Platform Reliability Engineer, you will be responsible for operating, maintaining, and continuously improving the company's data platforms running on Kubernetes (on-premises and/or on AWS/GCP) - similar to the DoEKS (Data on EKS) / AIoEKS (AI on EKS) deployment frameworks. Key responsibilities 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 About you 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 behaviour Well-rounded competency in analysing logs, metrics, monitoring systems, and service KPIs Exposure to other Data/AI platforms such as Flink, Trino, Druid, and Ray Hands-on experience with automation or scripting (Bash, Python) Kubernetes or Data certifications (CKAD, AWS Certified Data Engineer)