Staff Software Engineer, Iterable

• Hands-on Elasticsearch expertise in production:
• Supporting customer-facing features or platforms backed by ES
• Designing schemas and queries for high-cardinality, multi-tenant workloads
• Operating large clusters (capacity planning, scaling, incident response)
• Multi-tenant platform experience:
• Building shared data services used by multiple product teams
• Implementing rate limits, quotas, and noisy-neighbor protections
• Thinking in terms of contracts and SLOs, not one-off integrations
• Strong distributed systems background:
• JVM-based services (Scala/Java) and event-driven or streaming architectures
• Comfort with modern cloud infrastructure (AWS or similar), messaging, and caches
• Staff-level impact and collaboration:
• Leading cross-team projects from design through rollout
• Translating technical decisions into clear business and product trade-offs
• Clear written communication (RFCs, design docs, post-incident reviews)

Desirable

• Our Data In Flight (DIF) team owns the core pipelines that ingest and route user and event data into Iterable, powering identity, segmentation, and real-time personalization
• We are building a multi-tenant data platform that application teams can build on top of, with clear contracts, SLOs, and guardrails. Our product and our Elasticsearch clusters are multi-tenant, and we run Elasticsearch on Kubernetes using Elastic Cloud on Kubernetes (ECK)
• We’re looking for a Staff Software Engineer with a track record of operating Elasticsearch-backed data platforms in multi-tenant SaaS
• You’ll be a hands-on IC who both designs distributed systems and acts as a platform “product owner” for app teams: understanding their needs, shaping the platform roadmap, and defining stable interfaces between product and data infrastructure
• Own and evolve core Data In Flight services (ingestion, user and event APIs, identity, system events) with a focus on reliability, performance, and cost
• Lead the Elasticsearch strategy for our multi-tenant live user and event data: index and mapping design, shard strategy, and safeguards across shared clusters
• Design and enforce platform contracts (APIs, data models, SLAs, and usage limits) that let product teams move quickly while keeping the data platform safe and scalable
• Work across other data and application teams on initiatives such as Elasticsearch upgrades, guardrails, resharding, and event retention
• Provide senior technical leadership during incidents and reviews, turning operational learnings into durable platform improvements
• Mentor engineers on distributed systems, data modeling, and pragmatic use of Elasticsearch