The role
From Elastic's own posting.
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 precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.
What is the role?
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 Director, IT Engineering to join our Information Technology team to lead this effort. This is not a passive IT management role or a SaaS administration leadership position. It is a high-impact engineering leadership role where you will manage a small, agile team of engineers who look at $500,000 annual SaaS contracts, ask: 'Could we build a better version of this on Elasticsearch in twelve weeks?' — and execute.
This is a rare opportunity to lead 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.
What You Will Be Doing:
Lead strategic build-vs-buy engineering
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.
Own the architectural oversight, build plan, and overall delivery of the highest-priority replacements.
Lead a high-performing engineering team
Build, mentor, and lead a small, focused team of software engineers executing on internal tool replacements and Elastic-native platforms.
Manage delivery roadmaps, unblock your team, and balance engineering rigor with rapid execution speed.
Design & build Elastic-native internal Platforms: Guide your team in designing and delivering systems across five primary focus areas:
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, Legal and Operations, using the LLM Gateway, RAG pipelines, and Elasticsearch as the data and intelligence layer.
New use cases : New use cases that traditionally SaaS paid services did not address and can now be built with AI native architecture.
Set the engineering standard
Establish and own the architectural standards, design patterns, and engineering practices for all IT platform engineering work.
Define how internal systems are built — data models, API design, observability, cost governance, security posture — and serve as the engineering authority for your team.
Collaborate across the company
Partner closely with Elastic's IT infrastructure team, the Security organization (Elastic Security is your SIEM backbone), the Platform Engineering team, and Finance.
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 your team builds actually get used.
Dogfood and validate Elastic's own products
Run Elastic's own technology in the most demanding possible context: as the engineering leader responsible for the internal systems that the company depends on every day.
Every system your team builds is a reference architecture. Ensure that insights generated about what works and what doesn't feed directly into Elastic's product and go-to-market strategy.
What You Bring
Required experience
12+ years of software engineering experience, including experience managing or leading direct engineering reports or small engineering teams.
Hands-on familiarity and technical capability with Elasticsearch — data modeling, query design, aggregations, performance tuning, and production operations.
Proven track record of guiding full-stack engineering delivery end-to-end, from Logstash ingest pipelines to Kibana dashboards to React frontends.
Proven track record of building, shipping, and managing commercial software or platform systems — not just maintaining existing ones.
Architectural judgment: the ability to scope a problem correctly, choose the right components, and make executive calls on when to build versus integrate versus buy.
Strong written communication to author architecture decision records, engineering standards documents, executive proposals, and build-vs-buy analyses.
Valued experience
Experience leading teams that build 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 — understanding the core domain problems solved by platforms like Salesforce or ServiceNow.
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.
What This Role Is Not
Not a SaaS administrator or vendor manager: This is an engineering execution role, not a role for managing third-party vendor contracts, Salesforce admins, or ServiceNow developers.
Not a pure infrastructure or DevOps role: Your team uses cloud infrastructure, but the primary deliverable is production-grade software systems, not infrastructure configuration.
Not a hands-off hands-free manager: While you will manage a small team, you are expected to stay deep in the architecture, review code, and set technical directions hands-on.
Not a role for rigid processes: Projects begin with open-ended questions ('Could we build this?') and require you and your team to define and ship the answer.
Compensation for this role is in the form of base salary. This role does not have a variable compensation c