Senior Director, Engineering

 

What You'll Do 

Engineering Ownership & Execution 

• Own the engineering charter for Keystone's platform portfolio end-to-end, from architectural direction through delivery, DevOps, and iteration. 

• Translate IT/Product and business priorities into clear technical roadmaps, system designs, and prioritized engineering backlogs. 

• Set the standards for how systems are designed, deployed, secured, and operated across the estate, so quality does not depend on which team happens to be building. 

• Contribute directly to critical decisions and hands-on judgment on prototypes, technical spikes, and complex integration work when it accelerates outcomes. 

• Lead a development team on execution of product and platform development efforts, establish dev ops practices and telemetry to monitor performance and identify issues proactively.  

Architecture, Scale & Resilience 

• Own the multi-year technical architecture for Keystone's platform portfolio — the SaaS ecosystem, cloud infrastructure, data platform, integration fabric, and internally-built products — and evolve it as the firm scales into new geographies, practices, and consultant volumes. 

• Establish service level targets — availability, latency, recovery, data integrity — that reflect the criticality of each system to consultant delivery, client engagement, and corporate operations. 

• Drive a durable investment program in resilience: redundancy, failover, backup and recovery, disaster-recovery drills, capacity planning, and the retirement of brittle or single-threaded components before they fail under load. 

• Partner with Security and Compliance on architecture decisions that touch client data, cross-border data flows, identity, and access — making the secure path the default path. 

AI Strategy & Enablement 

• Invest thoughtfully — favouring durable capability, measurable leverage, and responsible use over adoption for its own sake. Frame AI investment in terms of specific outcomes: hours returned to consultants, quality lifted, time-to-answer reduced, manual tasks eliminated. 

• Build and operate the internal platform that makes AI secure, cost-aware, and accessible — model access, prompt and context management, evaluation harnesses, guardrails, observability, and the connective tissue between AI and the firm's systems of record. 

• Establish patterns for embedding AI into consultant-facing and operational workflows so that adoption is fast, quality is measurable, and human review sits where it belongs. 

• Participate as a member of the AI Governance Committee for direction, policy and execution of the governance model for AI use — data handling, model choice, client-work considerations, and the internal policies that guide when and how AI is applied. 

Integrations & DevOps 

• Own the integration fabric that connects Keystone's SaaS platforms, internally-built products, data warehouse, and AI capabilities into a coherent operating environment — so that a change of a client, project, or employee flows once and is reflected everywhere it needs to be. 

• Establish integration patterns and standards (event-driven, API-first, idempotent, observable) that reduce point-to-point coupling and make new integrations cheaper and safer to build over time. 

• Own the end-to-end DevOps lifecycle across engineering teams — source control, CI/CD, environment management, infrastructure-as-code, release management, and post-deployment monitoring — with the aim that changes ship frequently, safely, and with fast rollback when needed. 

• Set uptime targets and integration reliability SLOs; instrument systems to make performance measurable and issues detectable early. 

• Curate the middleware, iPaaS, ETL/ELT, and event-streaming tooling used across the firm; make deliberate build-vs-buy decisions and retire redundant tools. 

Issue Management & Support 

• Own the issue management and resolution pipeline end-to-end — from intake, through triage, engineering resolution, and communication back to affected users — with clear ownership, SLAs, and reporting to firm leadership on health and trends. 

• Build a mature incident management practice: on-call rotations, severity definitions, response protocols, communication standards, postmortems, and a feedback loop that reduces repeat incidents over time. 

• Partner with the leader of the IT Admin organization on the design and operation of the technical support model that consultants and operators experience day-to-day — tiered support, escalation paths, self-service where appropriate, and the tight coupling between support signal and engineering roadmap. 

• Establish and report on operational metrics — availability, mean-time-to-detect, mean-time-to-resolve, deployment frequency, change failure rate, integration SLOs, backlog age — that make the health of the estate visible and drive continuous improvement. 

Stakeholder & Business Partnership 

• Partner with Product Management to translate product strategy into technical roadmap, and hold a joint view of priorities, tradeoffs, and delivery risk. 

• Partner with consulting practice leaders, finance, HR, and operations leaders — so engineering decisions reflect how the business actually runs, and so business leaders understand the technical constraints and choices ahead of them. 

• Represent engineering outcomes to firm leadership: system health, integration risk, AI leverage delivered, incident trends, and the return on architecture investment. 

• Own vendor relationships for the platforms and infrastructure the team operates — driving accountability on roadmap commitments, uptime, integration quality, and cost. 

Team Leadership & Development 

• Build, lead, and develop the engineering organization — hiring, leveling, coaching, performance, and succession — across platform engineering, integrations, DevOps/SRE, and support functions. 

• Shape the operating model that lets a growing engineering team stay coherent as it scales: team topology, on-call practice, engineering rituals, technical decision-making, and the balance between central platform teams and product-aligned squads. 

• Set the bar for engineering craft, code quality, code review, testing discipline, and technical documentation, and hold the team to it. 

• Manage the mix of full-time engineers, contract engineers, offshore partners, and vendor implementation teams to deliver against the roadmap. 

• Own the engineering budget and forecast — cloud spend, SaaS spend, model spend, tooling, and people — and defend it credibly to firm leadership. 

Systems Thinking & Continuous Improvement 

• Design solutions that work across systems and reduce fragmentation between consultant delivery, client engagement, and back-office data. 

• Identify opportunities to introduce automation and AI to improve efficiency and reduce manual effort across the estate. 

• Improve systems based on usage patterns, incident trends, feedback, and evolving needs — including new geographic, regulatory, and practice-specific requirements as the firm expands. 

What You'll Bring 

• 10+ years of engineering experience, including 6+ years leading engineering organizations in environments with a broad SaaS estate, meaningful cloud footprint, and mission-critical integrations. 

• Track record of investing in architecture for scale and resilience — measurable improvements in availability, recovery, incident volume, or the ability to absorb growth without proportional operational cost. 

• Deep experience with integration architecture and the operational discipline required to run integrations well at scale — event-driven patterns, API design, data contracts, error handling, and observability. 

• Strong grounding in modern DevOps practice — CI/CD, infrastructure-as-code, observability, incident response, SRE principles — and demonstrated ability to raise the bar on these practices in a real organization. 

• Practical, current experience applying generative AI to real workflows, and a considered point of view on where AI creates durable leverage versus where it does not. 

• Hands-on comfort with hyperscaler cloud infrastructure and modern data platforms; ability to make credible architecture judgments without needing to be the deepest expert in any single system. 

• Track record building and running high-performing engineering support and issue-management functions that users trust. 

• Partnership-oriented leadership style — comfort operating alongside Product, Security, Data, and business leaders as peers, and credibility to represent engineering to firm leadership. 

• Strong written and verbal communication, including the ability to explain technical tradeoffs to non-technical stakeholders and to write clearly for decision-makers. 

Nice to Have 

• Experience in consulting, professional services, or another people-intensive services business where consultant leverage and utilization matter. 

• Experience operating engineering teams across multiple geographies and time zones, including EU data-residency considerations. 

• Experience building internal AI platforms — model access layers, evaluation frameworks, prompt and context management, cost controls — and running agentic AI systems in production. 

• Experience leading through an ERP, CRM, or PSA replacement, or comparable large-platform migration, without disrupting operations. 

• Experience owning a cloud FinOps discipline — visibility, budgets, unit economics, and cost accountability for cloud, SaaS, and model spend. 

• Experience partnering with Security and Compliance to achieve or maintain SOC 2, ISO 27001, or comparable certifications.