Principal Software Engineer

The enterprise software landscape is undergoing a structural shift. AI-assisted development has dropped the cost and time required to build custom internal tools by more than half. Retool's 2026 State of Internal Software report found that 35% of enterprises have already replaced SaaS tools with purpose-built software, and 78% plan to build more in the year ahead. The calculus has changed: what previously required a six-figure vendor contract and a year-long implementation is now a small engineering team and a few focused sprints.

Elastic is at the beginning of this journey. We operate the Elastic Stack — Elasticsearch, Kibana, Elastic ML, and a growing set of AI-native capabilities — at production scale for thousands of customers. And we are increasingly building on our own platform internally: replacing expensive SaaS tools with Elastic-native systems that are faster, more intelligent, and owned entirely by us.

We are looking for a Principal Engineer to join our Information Technology team to be part of this effort. This is not a support role or a tools administrator position. It is a senior engineering role for someone who can look at a $500,000 annual SaaS contract and ask: 'Could we build a better version of this on Elasticsearch in twelve weeks?' — and then go do it.

This is a rare opportunity to do original product engineering inside an IT organization, working with a platform that most engineers only get to use as a customer — and to build systems that directly shape how a public company operates.

Lead strategic build-vs-buy Engineering

You will work with IT leadership, Finance, and Engineering to evaluate Elastic's internal SaaS portfolio and identify where custom-built systems — backed by Elasticsearch, Kibana, and Elastic's AI capabilities — deliver better value than continued vendor contracts. You will own the architecture, engineering systems, build plan, and delivery of the highest-priority replacements.

Design and build Elastic-native internal platforms

The project portfolio spans four areas where the build-vs-buy shift is most compelling:

• Platform productivity tools — internal developer portals, knowledge search layers, engineering onboarding systems, and workspace tooling that unifies disparate SaaS tools into a single Elasticsearch-backed search and intelligence layer.
• FinOps and AI cost governance — a consolidated cost visibility and anomaly detection system across Elastic's AI providers (Anthropic, OpenAI Codex, GitHub Copilot, Google Gemini) and cloud platforms (AWS, GCP, Azure), replacing commercial FinOps SaaS with an Elastic ML-powered alternative.
• IT service management — evaluating and building replacements for commercially purchased SaaS  products including incident management, knowledge base search, internal developer portal, and SIEM-correlated security workflows, using Elastic Observability and Elastic Security as the backbone.
• Enterprise efficiency systems — custom tooling that reduces manual process overhead across IT, Finance, HR, and Operations, using the LLM Gateway, RAG pipelines, and Elasticsearch as the data and intelligence layer.

Set the engineering standard

As a Principal Engineer, you will establish and own the architectural standards, design patterns, and engineering practices for all IT platform engineering work. You will define how internal systems are built — data models, API design, observability, cost governance, security posture — and be the technical authority others look to when decisions get hard.

Collaborate across the company

You will partner closely with Elastic's IT infrastructure team, the Security organization (Elastic Security is your SIEM backbone), the Platform Engineering team, and Finance. You will engage with tool owners across the company to understand requirements, challenge assumptions about what needs to be bought versus built and ensure that the systems you build actually get used.

Dogfood and validate Elastic's own products

A significant part of the role is running Elastic's own technology in the most demanding possible context: as the engineer responsible for the internal systems that the company depends on every day. Every system you build is a reference architecture. Every insight you generate about what works and what doesn't feed directly into Elastic's product and go-to-market strategy.

• 15+ years of software engineering experience, with at least 3 years as a principal engineer.
• Deep, hands-on expertise with Elasticsearch — data modelling, query design, aggregations, performance tuning, and production operations. This is not a role for someone who has read the documentation; we need someone who has run Elasticsearch at scale and can debug it in production.
• Strong full-stack engineering capability: you can build and own a system end-to-end, from Logstash ingest pipelines to Kibana dashboards to a React frontend, without needing to hand off each layer to a different team.
• Proven track record of building and shipping commercial software or platform systems — not just maintaining existing ones. We want to see examples of systems you originated and delivered.
• Architectural judgment: the ability to scope a problem correctly, choose the right components, and make the call on when to build versus integrate versus buy. You have opinions about this, and you can defend them with data.
• Strong written communication. You will write architecture decision records, engineering standards documents, and build-vs-buy analyses. Your writing needs to be clear and persuasive to both engineering and non-engineering audiences.

• Prior experience building on the Elastic Stack beyond basic logging — Elastic ML, ELSER semantic search, vector search, Kibana Canvas, Watcher, or Elastic Security.
• Experience with FinOps practices: cloud cost management, LLM API cost attribution, or building cost visibility tooling.
• Familiarity with Information Technology managed systems — not necessarily Salesforce or ServiceNow expertise, but an understanding of the domain problems these tools solve.
• Experience working in a hypergrowth or post-IPO technology company where engineering rigor and business speed have to coexist.
• Experience with LLM integration patterns: RAG pipelines, prompt engineering for enterprise use cases, LLM gateway design, or AI-powered workflow automation.
• Contributions to internal developer experience, platform engineering, or engineering productivity tooling.