Head of Engineering

Base Operations•Washington, DC

7d•Hybrid

About The Position

Base Operations builds AI-powered physical threat intelligence software for global enterprises and U.S. federal/defense agencies. Our platform — including BaseEngine, BaseScore, and BaseCoPilot — helps security, risk, and operations teams understand and act on real-world threats faster than legacy intelligence tools allow. We're a dual-use company: the same core technology serves Fortune 500 security teams and U.S. government customers, which means our engineering org has to meet a high bar for both commercial velocity and federal-grade rigor. We're a Series A company scaling fast, and this role is foundational to how we build, ship, and scale engineering as a discipline — not just as a function.

Requirements

10+ years of progressive experience spanning software engineering, data architecture, and engineering management, with at least 3 years in a formal people-leadership role (Engineering Manager, Director, or equivalent).
Demonstrated experience owning data architecture decisions at scale — not just using data infrastructure, but designing it.
Strong program/delivery management instincts — comfortable owning timelines, measuring level of effort and cross-functional coordination with Eng & Product leads.
A genuine builder background — enough recent hands-on engineering experience to earn technical credibility with your team and make real architectural and development decisions.
Proven ability to hire, manage, and grow engineers, with a track record of building functional, high velocity teams.
Native fluency with modern AI tools — daily use of AI coding assistants/agents in your own workflow, and a point of view on how AI changes engineering org design, hiring, and velocity.
Comfort operating in a dual-use environment — security-conscious by default, and willing to learn the federal compliance landscape (FedRAMP, CMMC, CUI handling) as it applies to engineering.
Startup experience, ideally at Series A–B stage, where ambiguity is normal and the team is still being built.

Nice To Haves

Prior experience at a company serving both commercial and federal/defense customers.
Experience with threat intelligence, geospatial/OSINT data, or security-adjacent platforms.
Existing or prior eligibility for a U.S. government security clearance.
Experience standing up engineering processes from scratch at an early-stage company (not just operating within an existing one).

Responsibilities

Own the long-term data architecture strategy across our threat intelligence pipelines, ensuring systems scale cleanly as data volume, data types, customer count, and federal compliance requirements grow.
Make platform decisions (storage, pipelines, data modeling, database/data warehousing, ML/AI Ops) that balance speed, cost, and the dual-use constraints of serving both commercial and federal customers (e.g., data segregation, CUI handling, FedRAMP/CMMC-adjacent considerations).
Partner with product and GRC on data governance, lineage, and security-by-design as the platform matures.
Implement industry-standard program management discipline for engineering delivery — technical debt & product roadmaps, sprint cadences, dependency tracking, release velocity tracking, and risk surfacing — without slowing the team down with process for its own sake.
Own predictability: leadership and the board should be able to trust engineering's commitments and timelines.
Run cross-functional planning with Product and GTM to align engineering output with enterprise and federal sales cycles.
Stay technical and hands-on where it matters — code review, architecture decisions, and unblocking hard technical problems, not just status reporting.
Set and enforce engineering standards: code quality, testing, CI/CD, security practices, and technical documentation.
Drive decisions on system architecture, tech stack evolution, and technical debt prioritization.
Manage, mentor, and grow a team of engineers, including hiring, performance management, and career development.
Build a healthy engineering culture at a stage where the team is scaling quickly — clear ownership, high trust, low ego.
Act as the connective tissue between individual contributors and company leadership, translating technical realities into business terms and vice versa.
Bring native fluency in modern AI development workflows — using AI coding assistants, agentic tools, and LLM-based systems as a default part of how the team builds, not as a novelty.
Identify where AI can responsibly accelerate engineering velocity (code generation, testing, internal tooling) and where it introduces risk that needs guardrails (especially given our federal customer base).
Help define how Base Operations' own product uses AI under the hood — model selection, evaluation, reliability, and cost tradeoffs — in close partnership with product and applied data science talent.