Director of Engineering — FairPlay Sports Media Department: Technology / Engineering Reporting to: CTO Why this role exists FairPlay Sports Media is building a tech-led, AI- and data-powered sports media network that serves both owned brands (e.g., oddschecker, WhoScored.com, SuperScommesse, and others) and partners with BetTech products spanning data, display, and predictive capabilities. At FairPlay scale—real-time data, high-frequency updates, and partner integrations—you’ll lead multiple engineering teams to deliver reliable, compliant, high-throughput product execution. What you’ll do This role is modelled on modern Director of Engineering expectations: Your team is your product — you’re accountable for team health, hiring, delivery outcomes, and cross-functional alignment, while remaining technically credible enough to guide architecture and trade-offs. Lead and grow high-performing teams Lead multiple squads through Engineering Managers and senior ICs; build an org that can ship consistently across brands and partner-facing platforms. Own hiring strategy and capacity planning, focusing recruitment where it unlocks the most leverage. Coach Engineering Managers and senior engineers; create clarity, accountability, and psychological safety. Own delivery and execution at scale Be accountable for predictable delivery of roadmap commitments across BetTech products (Data, Display, Predictive) and shared platform capabilities. Increase throughput without sacrificing reliability—improving planning discipline, sequencing, and removing systemic blockers. Drive operational excellence for high-traffic, high-frequency systems (e.g., real-time odds/pricing and rapid data updates). Build strong cross-functional and partner interfaces Create a consistent, high-trust interface between Engineering and Product (and, as needed, Commercial/Partnerships), translating strategy into executable plans. Partner closely with data/AI stakeholders—especially as FairPlay expands predictive AI, pricing, and behavioral data-led products through FairPlay Technologies / FairPlay AI. Enable partner integrations and “white-label” deployments where required, ensuring a smooth path from contract → integration → measurable value. Set technical direction (without being the bottleneck) Guide architectural decisions across APIs, data pipelines, ML-enabled products, and front-end components/widgets—ensuring systems are secure, observable, and cost-effective. Sponsor engineering-wide standards where helpful, while allowing local variation where it accelerates outcomes. Ensure engineering practices cover the full DevOps lifecycle and modern CI/CD expectations. Run the org with measurable outcomes Define, measure, and improve the metrics that matter (delivery, reliability, quality, and org health). Establish lightweight governance: clear ownership, incident learnings, technical debt management, and documentation that stays current. What success looks like (example performance indicators) Hiring actual vs. plan for critical teams/skills. Delivery throughput & predictability: roadmap commitments met with stable scope/quality. Cycle time / lead time: reduced time from idea → production; fewer blocked work items. Reliability: improved uptime, incident rates, and mean time to restore (MTTR), especially for real-time feeds and partner-facing APIs. Data & latency SLAs: sustained performance at high-frequency update rates and real-time pricing/odds experiences. Documentation/operational readiness: runbooks, on-call hygiene, and key technical docs kept current. What we’re looking for: Required experience Proven experience leading multiple engineering teams (typically via Engineering Managers) in a product-led, high-availability environment. Strong systems and architecture judgment—able to broker high-level technical decisions and trade-offs across teams. Solid grasp of the DevOps lifecycle and delivery automation (CI/CD), with a track record of improving engineering productivity. Experience building platforms or products that integrate with external partners (APIs, SDKs, embeddable components), ideally in data-rich domains. Nice to have Experience in regulated or compliance-sensitive markets (or adjacent industries) with strong operational discipline. Exposure to data/ML product delivery (feature pipelines, model deployment patterns, experimentation frameworks), especially where AI-driven insights are core to the product. Familiarity with high-traffic consumer products alongside B2B SaaS/partner distribution models. Working style You bring clarity to ambiguous problems and drive execution/direction across functions. You communicate well up and down the org—aligning exec stakeholders while staying close enough to the work to unblock teams quickly. You care deeply about building a healthy, inclusive engineering culture where teams can do the best work of their careers.