The opportunity
Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the…
What you'll do
Design and build Elasticsearch platforms for customers across self-managed,: orchestrated and managed deployments, including air-gapped and regulated environments
Own sizing and capacity planning as a derivation from workload: characteristics — ingest rate, document shape, retention, query concurrency, growth — validated by benchmark rather than estimated from a form
Run health checks and remediation on production estates, and redesign: sharding, mapping and index strategies where the original design no longer holds
Lead major-version upgrades, snapshot and disaster recovery strategy, and multi-cluster architectures
Diagnose performance problems across the full stack: Elastic, the JVM, the operating system, storage and network — and across adjacent systems in the data path
Work directly with customer architects, platform owners and engineering: leadership; run discovery, present designs, and own the technical outcome of an engagement end to end
What they're looking for
- Build reusable tooling, automation and IP that makes the next engagement: faster, including AI-assisted tooling for diagnosis, configuration and reporting
- Mentor other consultants and contribute to the GCC's technical practice
- + years hands-on with the Elastic Stack, substantially on cluster and: platform work rather than dashboarding or pipeline authoring
- Shard strategy you can derive, not recite: how you arrived at a shard count and size for a given workload, what you traded off, and what you got wrong the first time
