Senior DevOps Engineer

Borr Drilling; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading company is seeking an Elastic Stack Architect to design and implement solutions using the Elastic Stack in both cloud and on-premises environments. This role involves managing Elastic clusters, creating dashboards, and utilizing AI/ML for IT operations. Candidates should have a Bachelor’s degree and strong experience with Elastic technologies, along with a deep understanding of cluster architecture and security practices.

Qualifications

• Strong hands-on experience with Elastic Stack (Elasticsearch, Logstash, Kibana, Beats).
• Deep understanding of Elastic cluster architecture, sharding, and replication.
• Experience with Logstash tuning (JVM heap, pipeline workers).

Responsibilities

• Design and implement Elastic Stack solutions for cloud and on-premises environments.
• Build and maintain Kibana dashboards for real-time monitoring and analytics.
• Use ML jobs for predictive IT maintenance and automated remediation.

Skills

Elastic Stack

Grok

Ruby

Data transformation techniques

DevOps tools

Education

Bachelor’s degree in Computer Science, Information Technology, or related field.

Job description

Key Responsibilities:

Elastic Stack Architecture & Deployment

• Design and implement Elastic Stack solutions (Elasticsearch, Logstash, Kibana, Beats) for both cloud and on-premises environments.
• Architect and manage Elastic clusters , including:
  • Cluster types (hot-warm-cold-frozen architecture)
  • Shard allocation and rebalancing
  • Index Lifecycle Management (ILM)
  • Snapshot and restore strategies
  • Cross-cluster search and replication
• Configure and fine-tune Logstash servers , including:
  • JVM heap size optimization
  • Pipeline tuning and throughput optimization
  • Persistent queues and dead letter queues
• Implement security best practices using Elastic Security features (TLS, RBAC, API keys).

Kibana Dashboards & Alerting

• Build and maintain Kibana dashboards for real-time monitoring and analytics.
• Define alerting rules using Watcher or Kibana alerting framework.

Elastic AI/ML & AIOps Use Cases

• Predictive IT Maintenance : Use ML jobs to detect anomalies and forecast incidents.
• Root Cause Analysis : Correlate logs and metrics to identify root causes.
• Intelligent Escalation : Trigger alerts with probable causes and remediation steps.
• Automated Remediation : Integrate with ITSM tools for ticketing and automated fixes.
• Proactive Capacity Resizing : Use time-series forecasting to predict and scale resources.
• Event Correlation & Noise Reduction : Group related events to reduce alert fatigue.
• Adaptive Baselining & Thresholding : Automatically generate baselines and thresholds.

Required Skills & Qualifications:

• Bachelor’s degree in Computer Science, Information Technology, or related field.
• Strong hands-on experience with Elastic Stack (Elasticsearch, Logstash, Kibana, Beats).
• Deep understanding of:
  • Elastic cluster architecture
  • Sharding and replication
  • Index Lifecycle Management (ILM)
  • Hot, warm, cold, and frozen node tiers
  • Snapshot and restore
  • Elastic Security and role-based access control
• Experience with Logstash tuning (JVM heap, pipeline workers, persistent queues).
• Proficiency in Grok , Ruby , and data transformation techniques.
• Familiarity with DevOps tools (CI/CD, Git, Docker, Kubernetes).
• Experience with Elastic ML features or similar ML tools for observability.

Preferred / Added Advantages:

• Participation in data analytics-related innovation programs , internships , or competitions .
• Experience with cloud platforms (AWS, Azure, GCP) and Elastic Cloud.
• Exposure to ITSM tools (e.g., ServiceNow, Jira Service Management).
• Elastic certifications (e.g., Elastic Certified Engineer) are a plus.