Senior Data Platform Reliability Engineer
Summary
Operates, maintains, and continuously improves the company's data platforms on Kubernetes (on-prem and AWS/GCP EKS/GKE), handling GitOps deployments, monitoring, incident response on 24x7 rotations, and troubleshooting user issues while mentoring junior engineers. Core stack includes Kubernetes, Linux, and ETL/ELT tooling like Spark, Airflow, and Python/SQL.
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)