Senior Director of Platform & Data Engineering

Keyloop is a leading automotive retail software business, operating across 100+ markets and backed by Francisco Partners. As we accelerate our transition to a modern, cloud-native SaaS platform, Platform & Data Engineering sits at the heart of our technical strategy. This is a high-impact leadership role reporting directly to the CTO, responsible for setting and executing the platform strategy that enables Keyloop's product engineering teams, powers our data and AI capabilities, and provides the integration fabric for an acquisitive, multi-product business.

Responsibilities

Lead and develop a diverse, distributed team of platform and data engineers, providing guidance, mentorship, and clear career pathways to ensure high performance and professional growth; Build and refine the organisational structure of the platform function, including defining team topologies that support product engineering teams as internal customers; Foster a culture of engineering excellence, collaboration, and continuous improvement, with a strong emphasis on developer productivity and platform reliability; Ensure security, stability, and scalability are foundational properties of the platform; Co-own the multi-year platform engineering roadmap with the Platform Product Leader; Lead the evolution of Keyloop's shared services, API gateway, integration layer, and internal developer platform (IDP); Establish API-first as a non-negotiable architectural principle; Drive cloud-native architecture decisions, leveraging AWS services and infrastructure-as-code practices (Terraform/Pulumi); Champion platform security-by-design; Architect and evolve the integration platform that enables Keyloop to onboard acquired businesses and codebases efficiently; Define and maintain shared platform capabilities including identity, authentication, multi-tenancy, data residency, and OEM connectivity standards; Design and deliver low-code/no-code integration capabilities that enable rapid, standardised connectivity with third-party systems; Lead the design and delivery of Keyloop's data platform, including data lakes, data pipelines, and analytical infrastructure; Build and operate the infrastructure layer for AI and ML workloads, including feature stores, model serving infrastructure, MLOps pipelines, and experimentation frameworks; Drive strategic adoption of technologies including Snowflake, Databricks, AWS Athena, EMR, Glue, Snowplow, and Kafka; Partner closely with product and data science teams to ensure the data and AI platform directly enables Keyloop's AI product initiatives; Lead Keyloop's transition to an agentic software development lifecycle; Build and own the internal platform capabilities required to support agentic workloads, including MCP server infrastructure, LLM gateway services, context management systems, and agent orchestration layers; Establish guardrails, security controls, and governance frameworks that allow teams to move fast with AI tooling; Drive and report on engineering AI maturity progression; Oversee the execution of platform engineering initiatives, ensuring timely delivery, adherence to quality standards, and effective resource utilisation; Define and embed observability standards across the platform (OpenTelemetry, distributed tracing, SLO/SLA frameworks); Drive FinOps discipline across platform infrastructure; Collaborate closely with the CTO, engineering directors, architects, and product leadership; Represent platform and data engineering at the executive and board level

Requirements

Deep experience in cloud-native platform engineering on AWS, including IaC (Terraform or Pulumi), containerisation (Kubernetes/ECS), event streaming (Kafka), and API gateway patterns; Proven track record designing and operating shared platform services - integration layers, identity, multi-tenancy, and developer-facing APIs - in a complex, multi-product enterprise SaaS environment; Hands-on background in data platform engineering: data lake architecture, ELT pipelines, and familiarity with Snowflake, Databricks, and AWS data services (Athena, Glue, EMR); Experience building AI/ML infrastructure: feature stores, model serving, MLOps pipelines, and LLM-enabling platform capabilities such as MCP server infrastructure or LLM gateway services; Strong understanding of platform observability: OpenTelemetry, distributed tracing, SLO/error budget frameworks, and production reliability engineering; Security-first mindset with hands-on experience in enterprise security practices, compliance frameworks (ISO 27001, SOC 2), and zero-trust architecture patterns; Extensive engineering leadership experience, with a proven record of building, scaling, and developing high-performing distributed engineering teams of 60+ people; Experience operating within an acquisitive software business, with the ability to lead M&A technical integration and platform standardisation across acquired codebases and teams; Familiarity with modern delivery frameworks (SAFe, Shape Up, or equivalent) and the ability to adapt methodology to the needs of a platform organisation serving multiple internal customers; Strong FinOps capability, including cloud cost governance, unit economics thinking, and the ability to frame infrastructure investment in commercial terms; Practical experience adopting and scaling AI-native development tooling (e.g. GitHub Copilot, Claude Code, Cursor, or equivalent) across engineering organisations; Understanding of agentic software delivery patterns: autonomous agents, human-in-the-loop workflows, agentic code review, test generation, and AI-assisted architecture; Ability to define governance frameworks that enable AI-augmented development at pace while managing code quality, IP protection, and data security risks; Passion for driving engineering culture change, with the ability to take a sceptical or early-majority engineering team on a credible AI adoption journey; Excellent communication skills, with the ability to translate complex platform and data engineering decisions into clear narratives for executive, commercial, and board audiences; Strong cross-functional collaboration skills, with experience partnering with product, commercial, and finance leadership in a PE-backed, high-growth software environment