Own and operate large-scale Elasticsearch production clusters, serving as the L3 escalation point for performance, stability, ingestion, JVM, shard, and infrastructure issues. Deploy and manage Elastic on Kubernetes and cloud or on-premises platforms, optimize Linux systems, integrate observability pipelines, perform root-cause analysis, and implement permanent fixes. The role also establishes operational standards, mentors engineers, collaborates with customers and Elastic support, and provides architectural recommendations.
The Position
Ashnik is
looking for a highly skilled L3 Elastic Observability Engineer to join our
Technology Solutions Team.
This is a
senior, hands-on role for engineers who have designed, built, and stabilized
Elastic clusters from scratch, handled complex performance issues, and acted as
the final technical escalation point.
You will
manage large-scale production deployments, solve deep performance and
architecture issues, and collaborate closely with customers and internal teams
to ensure reliability, scalability, and long-term success of Elastic-based
observability platforms.
This role
demands strong technical depth, structured problem-solving, and clear
communication.
Qualifications
- 5 to 7 years of hands-on experience working with Elasticsearch in production environments.
- Proven experience managing large and complex Elasticsearch clusters in production.
- Strong hands-on experience with Elasticsearch internals, indexing, and search performance tuning.
- Hands-on experience with shard design, ILM, rollover, and reindexing.
- Experience managing Elasticsearch security, authentication, authorization, and access control.
- Solid experience with Kubernetes, including deployment, scaling, upgrades, and troubleshooting.
- Strong hands-on experience with Linux OS, networking, storage, and system-level troubleshooting.
- Experience handling high-volume ingestion and large datasets.
- Ability to analyse logs, metrics, and traces to diagnose and resolve complex system issues.
- Strong communication skills, with the ability to explain technical issues clearly to customers and internal teams.
- High sense of ownership and accountability for problem resolution.
Requirements
Key Responsibilities
- Own and manage large-scale Elasticsearch
clusters in production, ensuring high availability, reliability, scalability,
and optimal performance.
- Monitor and maintain cluster health, node
performance, shard allocation, storage, JVM, memory, and resource utilization, onboarding workloads and deploying APM
- Performance & Troubleshooting
Act as L3 escalation for complex production issues related to:
cluster instability
indexing/search performance
JVM, heap, GC, and memory pressure
shard imbalance and hot nodes
query and ingestion bottlenecks
Perform deep root-cause analysis and drive permanent fixes—not workarounds.
Tune Elasticsearch for high-throughput, low-latency workloads. - Platform
& Infrastructure: Work
extensively on Linux-based systems for performance tuning, networking, and
storage optimization. Deploy and
operate Elastic on Kubernetes environments (ECK or custom setups). Integrate
Elastic with ingestion pipelines using Logstash, Beats, Elastic Agent, and
other data sources.Support
deployments across on-prem and cloud platforms (AWS / Azure / GCP).
- Operational
Excellence: Define
and enforce best practices, standards, and documentation for Elastic
deployments. Mentor
L1/L2 engineers and review their designs, fixes, and operational decisions. Collaborate
with Elastic support and internal product teams on complex cases.
- Communicate
clearly with customers on issues, resolutions, and architectural
recommendations.
Nice to Have
- Experience
with greenfield implementation.
- Exposure
to SIEM or security analytics use cases.
- Experience
working in customer-facing support or consulting roles.
- Scripting
experience in Python / Bash for automation and diagnostics.
- Elastic
certifications (or willingness to pursue them).
Benefits
Click here to learn more about the benefits
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