Engineering Director

About the job

We're hiring an Engineering Director to lead multiple engineering teams within our platform organization. Reporting directly to the CTO, you'll own the technical direction, delivery, and operational health of your area — setting architecture, growing engineers,
and making the trade-off calls that keep us fast without accumulating debt we can't pay back.

This is a hands-on leadership role. You won't be writing the majority of the code, but you will be close enough to it to review a tricky PR, debug a production incident alongside your team, and hold a credible opinion in a design review. Engineers should want to work for you because you make their work better, not just because you unblock it.

It's also a startup role, and we want to be direct about what that means. Priorities shift. Scope changes as we grow. Process that worked at ten engineers breaks at thirty, and you'll be the one deciding what to replace it with. You'll ship things that aren't finished, then come back and finish them. If you need a stable org chart and a locked annual roadmap to do your best work, this won't be a good fit — and if you thrive on building the plane
mid-flight without pretending that's easy, it will be.

We operate as eight functional teams:

• Data Pipelines - Python workers with a Go normalization service

• Streaming Analytics & Detection - Go, Rust and Java stream processing and detection engines.

• Data Search Analytics - Rust-based search backend

• Platform - API Platform - python & fast-api

• UI - React application

• AI Analysis - AI frameworks and agentic analysis

• Integrations - Python integration and connector framework

• DevOps / SRE - Infrastructure, Kubernetes, reliability and CI/CD

This role can take several shapes depending on your background. We'd rather find the right leader and build the scope around them, than force a fit. Possible paths could be:

• Data Path - Data Ingest, Streaming Analytics & Detection, and Data Search Analytics. The highest-throughput, lowest-latency part of the platform. Best fit for leaders with deep distributed systems, stream processing experience, and experience in security or fraud analytics.

• Application & Platform Path - Platform (API), UI, and DevOps. Product surface area, developer experience, and the connector ecosystem. Best fit for leaders who've shipped customer-facing platforms and APIs at scale, and owned platform reliability or led DevOps teams before.

• AI & Analytics Path - AI Analysis alongside Detection and Search. Applied AI on top of security data. Best fit for leaders who've productionized ML/LLM systems, not just prototyped them.

What You'll Do

• Own technical strategy and roadmap execution for your teams, in partnership with Product Management, the CTO, VPs of Engineering, and Engineering Leads.

• Lead, mentor, and grow engineering leads and managers — and hire well, because most of your leverage comes from who you bring in.

• Set and enforce a high bar for system design, code quality, testing, and operational rigor

• Make architectural decisions with real trade-offs: performance vs. cost, velocity vs. durability, build vs. buy. Then document the reasoning so the next person understands it.

• Drive capacity planning, infrastructure sizing, and performance tuning for systems handling high-volume security telemetry.

• Own operational excellence for your area — SLOs, on-call health, incident response, and the follow-through that keeps the same incident from happening twice.

• Work across team boundaries. Our teams are functional, but our features are not; you'll spend real time coordinating with peers who own adjacent parts of the platform.

• Scale the engineering practices themselves — planning, on-call, code review, and hiring loops — as the team grows, adding structure where it earns its keep and resisting it where it doesn't.

• Translate technical reality for non-technical stakeholders, including customers and executive leadership.

About you

Required

• Experience building and shipping production software, with at least 3 years leading engineering teams (including experience managing managers or senior/staff-level engineers).

• Demonstrated technical depth in at least one of our core areas: distributed data systems, API platforms, search, applied AI, or large-scale infrastructure.

• Strong proficiency in one or more of Go, Rust, Python, or Java ; the ability to read and reason about code in the others, and an excitement to get deeper into the Rustacean world.

• Experience operating systems at scale in Kubernetes, including capacity planning, performance tuning, and production incident response.

• A track record of shipping. You can point to systems you led that are running in production today and explain what you'd do differently now.

• Strong architectural judgment and a bias toward pragmatic decisions over elegant ones.

• Excellent written and verbal communication. Much of your influence will be asynchronous and in writing.

• Experience hiring, developing, and managing a team around you was growing fast.

• Comfort operating with incomplete information, shifting priorities, and less process than you'd like. Startup or early-stage experience is strongly preferred.
  
  Nice to Have

• Experience in cybersecurity, or with SIEM, SOAR, or XDR products.

• Experience with streaming frameworks (Kafka Streams, Flink, Spark Streaming, Beam, Arroyo) and a real understanding of windowing, state management, checkpointing, and exactly-once semantics.

• Experience with multiple datastore paradigms — full-text search (Elasticsearch, OpenSearch), columnar/analytical stores (ClickHouse, Druid), time-series databases, and graph dbs.

• Experience building detection engines, correlation rules, or real-time alerting systems.

• Experience building integration or connector frameworks that normalize data from diverse sources.

• Experience productionizing LLM-based or ML-based analysis systems.

• Experience taking an engineering org through a significant scaling inflection — 20 to 50 engineers, or a similar jump.