Case study
Elasticsearch Optimization for an HR SaaS Platform
How SquareShift delivered ILM, sharding, and Kibana best practices to an HR SaaS platform serving thousands of organizations.
Handing a team a runbook doesn't change behavior. Pairing ILM and sharding best practices with live training is what actually sticks.
Strategy & Decision Intelligence Practice Lead, SquareShift
A cloud-based HR SaaS platform used by thousands of organizations to manage payroll, scheduling, time tracking, and talent management.
It needed stronger Elasticsearch practices to keep search and reporting reliable at that scale.
- Standardized ILM — Unified index lifecycle and sharding strategy across every module.
- Kibana governance — Structured dashboards, access control, and API key management put in place.
- Built for scale — Tuned to support thousands of organizations' payroll, scheduling, and talent data.
How SquareShift delivered it.
The challenge
The client’s cloud-based HR SaaS platform supports payroll, scheduling, time tracking, onboarding, learning, and performance reviews for thousands of organizations. Its search configuration had grown fragmented across modules, with no standardized approach to index lifecycle management or sharding, and limited visibility into resource usage.
What we delivered
SquareShift ran a tailored workshop series covering ILM strategy, cluster sizing, transforms, and Kibana object organization, paired with AWS integration guidance. The team introduced best practices for index templates and API key management, and guided the platform team through structuring dashboards and access control.
The payoff
The platform now runs on a standardized ILM and sharding strategy across every module, with governed Kibana dashboards and access control in place. That gives the team a consistent, scalable foundation for search and reporting as its organization base keeps growing.
Where this work sits