Director – Engineering - Analytics & Data Services

We're looking for a Director of Engineering to lead Analytics & Data Services within Recorded Future's Intelligence Graph organisation. This group owns the layer that turns the graph into something other systems can actually run on: the data services and APIs that expose it, the AI data services and custom datasets we ship as products, and the analytics platform that measures whether any of it is any good.

The context is a genuine shift in what the graph is for. Recorded Future is becoming a company that creates and ships agentic software at scale, and every agent we ship reads from the Intelligence Graph. That moves the graph from a backend behind product features to the intelligence engine of the company. It also changes the standard it's held to: data availability becomes product availability, and data quality becomes product quality. Your organisation is where both of those are made real — the availability layer that lets agents reach our data, and the measurement layer that makes it trustworthy enough to act on.

You'll lead through managers and senior technical leaders, own the roadmap for the area, and be one of the people accountable for whether the graph organisation's transformation actually delivers. Part of the job in your first year is integration: the Analytics Framework and Signals teams are merging, and you'll be shaping the combined organisation rather than inheriting a settled one.

What You'll Do:

• Lead a multi-team organisation through engineering managers and technical leads, with accountability for delivery, quality and the health of the teams
• Own the strategy and roadmap for Data Services, Signals & Analytics Framework, and the Analytics Platform — and defend the sequencing when priorities compete
• Aim Data Services squarely at the Agentic Platform: make graph data available, fresh and measured to the standard agentic software requires, exposed as products rather than internal plumbing
• Build the measurement capability that makes automation trustworthy at scale — instrumentation of quality, coverage, freshness and drift across the graph, used to tune systems rather than to write reports
• Lead the merger and integration of the Analytics Framework and Signals teams into a coherent organisation with one mandate
• Hire, develop and retain engineering managers; build the bench beneath you and hold a high bar on both leadership and technical judgment
• Partner closely with Curation Automation, Graph Quality, Collection, Product and the wider Platform organisation — much of your impact lands through other people's teams
• Own headcount and budget for your area, and make honest trade-offs inside it
• Represent the area to executive leadership: what's working, what isn't, what it will cost, and what you need decided

What You'll Bring:

• Around 8+ years of engineering leadership experience, including 3+ years managing managers
• A background as a software engineer, in data platforms, distributed systems or analytics infrastructure
• Track record of setting technical and organisational direction for a multi-team area, not just executing a roadmap handed down
• Experience building or running data platform capabilities consumed by other teams or by customers — APIs, data services, datasets, measurement infrastructure
• Demonstrated ability to lead organisational change: mergers of teams, shifts in mandate, transitions where the old way and the new way run in parallel for a while
• Strength in hiring and developing engineering managers, and in handling performance issues among leaders
• Comfort with quality as a measured discipline — you expect claims about data to come with numbers attached
• Excellent verbal and written communication, with the ability to hold a position with executives and explain a technical trade-off to a commercial audience
• Bachelor's and/or Master's degree in Computer Science, Computer Engineering, or equivalent education/work experience
• Strong English skills are a must

Preferred Qualifications:

• Experience shipping data or intelligence products externally, including the commercial and packaging side
• Domain knowledge in cyber security and the cyber-defence data model
• Experience taking LLM- or agent-based systems into production at scale, including honest evaluation practice
• Having built the measurement layer for an automated system — evaluation harnesses, quality telemetry, drift detection
• Experience leading distributed organisations across sites and time zones
• Experience working in a company undergoing a strategic platform shift, on the side that had to deliver it