Senior Data Platform Reliability Engineer
Senior engineer maintains and improves production data platforms on Kubernetes across on-prem, AWS, and GCP, using GitOps and observability tools to ensure reliability and scale.
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Full time
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 3+ years supporting production data platforms or workloads using Spark, Airflow, or Jupyter
- 5+ years hands-on ETL/ELT pipelines and data transformations using Python or Java and SQL
- Strong practical experience with Kubernetes, including AWS EKS or Google GKE
- Strong knowledge of Linux environments, microservices architectures, and service communication patterns
- Solid troubleshooting skills across application crashes, resource contention, service latency, performance, and scaling issues
- Experience analyzing logs, metrics, monitoring systems, and service-level KPIs
Nice to have
- Experience with Flink, Trino, Druid, or Ray
- Hands-on scripting and automation using Bash or Python
- Relevant Kubernetes, cloud, or data certifications such as CKAD or AWS Certified Data Engineer
Full description
Our client is a technology consulting company providing operational and engineering services to the high-tech sector. They support platform and infrastructure teams in managing multi-cloud environments, delivering complex migrations, and enabling reliable application deployments at scale.
The Role
Our client is looking for a Senior Data Platform Reliability Engineer to operate, maintain, and continuously improve production data platforms running on Kubernetes across on-premise, AWS, and GCP environments.
You will work across platform reliability, observability, automation, incident management, and data infrastructure, supporting environments similar to Data on EKS and AI on EKS architectures.
Key Responsibilities
- Deploy platform releases and configuration updates using GitOps and DevOps practices.
- Monitor platform and service health through logs, metrics, monitoring, and observability tooling.
- Participate in incident response, root cause analysis, and 24/7 operational rotations.
- Improve platform reliability through better observability, automation, operational tooling, and self-service capabilities.
- Investigate and troubleshoot user and platform issues, including system failures, broken integrations, configuration problems, and application-level errors.
- Recommend and implement appropriate technical resolutions.
- Mentor junior engineers and support the development of engineering best practices.
- Promote strong standards across platform operations, security, reliability, and engineering quality.
Qualifications
- 3+ years of experience supporting production data platforms or workloads using technologies such as Spark, Airflow, or Jupyter.
- 5+ years of hands-on experience developing ETL/ELT pipelines and data transformations using Python or Java and SQL.
- Strong practical experience working with Kubernetes, including managed Kubernetes platforms such as AWS EKS or Google GKE.
- Strong knowledge of Linux environments, microservices architectures, and service communication patterns.
- Solid troubleshooting skills across application crashes, resource contention, service latency, performance, and scaling issues.
- Experience analysing logs, metrics, monitoring systems, and service-level KPIs.
Nice to Have
- Experience with additional Data or AI platforms such as Flink, Trino, Druid, or Ray.
- Hands-on scripting and automation experience using Bash or Python.
- Relevant Kubernetes, cloud, or data certifications such as CKAD or AWS Certified Data Engineer.