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

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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,

PagerDuty, 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 (SecOps), 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

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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 (OpenTelemetry, 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 (SecOps) and detection use-cases: threat hunting,

anomaly detection, SOC workflows.

•

Familiarity with infrastructure instrumentation (logs, metrics, traces) and analysing

telemetry from microservices/distributed systems.

Originally posted on Himalayas