Elastic Stack Engineer

Job Description: Elastic Stack Engineer (Search & Observability) Role Overview As an Elastic / Observability & Security Platform Engineer, you will lead the design, implementation, monitoring and continuous improvement of our Elastic-based observability and security stack. You will take ownership of detection rules, watchers, ML-models, health monitoring of data streams, alerting frameworks, and tracking of data pipeline latency/integration times.

You will work closely with data engineers, security operations, platform engineering, and business-units to ensure robust real-time monitoring, anomaly detection, alerting, and data integration observability.

Key Responsibilities

• Architect, deploy, configure and optimise the Elastic Stack (Elasticsearch, Kibana, Beats, Logstash, Elastic Machine Learning, Elastic Watcher/Alerting).
• Develop and maintain JSON-based configuration files, logic and pipelines for detection rules, watchers and alerting states.
• Design, build and operationalise machine-learning jobs within Elastic ML (e.g., anomaly detection, forecasting, classification) for observability/security use-cases.
• Monitor, maintain and improve the health and performance of data-streams (logs, metrics, events, traces) ingesting into the Elastic cluster: ensure data freshness, minimal latency, correct mapping, index lifecycle management (ILM), shard management, and cluster health.
• Implement and maintain alerting/notification frameworks: watchers/triggers, custom alert-logic via JSON, integration with downstream systems (Slack, Teams, Pager. Duty, email, webhook).
• Track and report on the integration time between upstream data sources and the Elastic ingestion pipeline (i.e., latency from source → pipeline → index → availability), diagnose and mitigate delays or bottlenecks.
• Develop dashboards, visualisations and reports in Kibana to communicate KPIs, SLAs (data-ingestion, alert-response, model accuracy), and to drive continuous improvement.
• Collaborate with data engineering, DevOps, security operations (Sec. Ops), SRE and business stakeholders to define requirements and deliver effective observability/security solutions.
• Establish best‐practices, standards and documentation for JSON rule-configs, watchers, ML-jobs, dashboarding and monitoring.
• Participate in incident-response processes: support triage, root-cause analysis and feed learnings back into detection rules/ML jobs/monitoring.
• Stay up-to-date and contribute to improving the Elastic ecosystem in our environment: new features, upgrades, tuning, cost-optimisation, benchmark/scale testing.

Required Skills

& Experience

• Strong hands-on experience with the Elastic Stack (Elasticsearch, Kibana, Beats, Logstash or equivalent ingestion pipelines) – you should be comfortable deploying, configuring and operating production Elastic clusters.
• Proficiency in writing and using JSON configurations and logic for detection rules, watchers, alerting frameworks, and monitoring pipelines.
• Experience building and operationalising Elastic Machine Learning jobs (anomaly detection, forecasting, classifications) and interpreting model output for observability/security use-cases.
• In-depth experience monitoring and maintaining the health of high-volume data streams: log/metric/event/tracing data, with attention to data latency, ingestion batching, pipeline failures, index lifecycle, and cluster resource optimisation.
• Experience designing end-to-end alerting workflows (trigger logic, thresholds, multi- condition rules, escalation, notification integration).
• Experience tracking and measuring integration times (data latency from source ingestion to availability in index/dashboards) and implementing improvements to reduce that latency.
• Strong scripting or programming ability (e.g., Python, Bash, or similar) to automate tasks, integrations or alert-logic.
• Strong analytical and problem-solving skills: ability to diagnose ingestion/pipeline/cluster issues, chain of events, root causes, and propose mitigations.
• Excellent communication skills: able to articulate detection logic, ML-model results, data‐latency issues and dashboards to technical and non‐technical stakeholders.
• Good understanding of DevOps/SRE practices (CI/CD, Infrastructure as Code, Monitoring, Logging, Alerting).
• Ability to document clearly: JSON rule setups, watchers, dashboards, models, runbooks.
• Bachelor’s degree in Computer Science, Information Systems or equivalent experience; or equivalent relevant industry experience. Desirable / Bonus Skills
• Experience with elastic security (formerly SIEM) use‐cases using Elastic.
• Experience with other observability/tracing stacks (Open. Telemetry, Jaeger, Prometheus, Grafana) and integrating them into Elastic.
• Knowledge of cloud environments (AWS, Azure, GCP) and experience managing Elastic clusters in cloud or hybrid deployments.
• Experience with large scale index management, shard tuning, ILM policies, cluster scaling, and cost optimisation.
• Experience with advanced ML-techniques (unsupervised learning, time‐series forecasting, advanced feature engineering) applied to observability/security.
• Knowledge of security operations (Sec. Ops) and detection use-cases: threat hunting, anomaly detection, SOC workflows.
• Familiarity with infrastructure instrumentation (logs, metrics, traces) and analysing telemetry from microservices/distributed systems.