平台与数据工程高级总监

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:

1.  Leadership & Team Development

• 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.

2.  Platform & Technology Strategy

• Ensure security, stability, and scalability are foundational properties of the platform - not afterthoughts - with clear ownership of reliability targets, security-by-design standards, and the capacity to serve a growing SaaS customer base across 100+ markets.

• Co-own the multi-year platform engineering roadmap with the Platform Product Leader, translating commercial and product priorities into a coherent engineering delivery plan.

• Lead the evolution of Keyloop’s shared services, API gateway, integration layer, and internal developer platform (IDP) to serve as the technical foundation across all product lines.

• Establish API-first as a non-negotiable architectural principle - every platform capability must be accessible via a well-governed, versioned API surface before any other access pattern is considered, enabling both internal product teams and external ecosystem partners to build reliably on top of Keyloop infrastructure.

• Drive cloud-native architecture decisions, leveraging AWS services and infrastructure-as-code practices (Terraform/Pulumi) to ensure scalability, resilience, and cost efficiency.

• Champion platform security-by-design, ensuring adherence to enterprise security standards, compliance frameworks (ISO 27001, SOC 2), and Keyloop's obligations as a custodian of sensitive automotive retail data.

3.  Integration & Acquisitive Platform Capability

• Architect and evolve the integration platform that enables Keyloop to onboard acquired businesses and codebases efficiently, reducing time-to-integration across M&A activity.

• Define and maintain shared platform capabilities including identity, authentication, multi-tenancy, data residency, and OEM connectivity standards.

• Ensure the platform acts as the enabling layer across Keyloop’s product portfolio, providing consistent APIs, event streaming, and shared infrastructure rather than duplicating capability across product teams.

• Design and deliver low-code/no-code integration capabilities that enable rapid, standardised connectivity with third-party systems across the automotive ecosystem, including OEM partners, dealer group platforms, and acquired businesses. Build on modern integration tooling (including n8n) to reduce time-to-integration and enable non-engineering teams to configure and operate integrations where appropriate.

4.  Data & AI Platform

• Lead the design and delivery of Keyloop's data platform, including data lakes, data pipelines, and analytical infrastructure to power BI, reporting, and AI-driven product capabilities.

• 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, ensuring robust data governance and quality practices.

• Partner closely with product and data science teams to ensure the data and AI platform directly enables Keyloop's AI product initiatives, including KARA and AIME.

5.  Enabling the Agentic Development Lifecycle

• Lead Keyloop's transition to an agentic software development lifecycle, defining the strategy, frameworks, and delivery model for AI-augmented engineering at scale.

• Develop and roll out the methodologies, toolchains, and workflows that enable engineering teams to work with AI coding agents, autonomous test generation, agentic PR review, and AI-assisted architecture decisions.

• Build and own the internal platform capabilities required to support agentic workloads, including MCP (Model Context Protocol) server infrastructure, LLM gateway services, context management systems, and agent orchestration layers.

• Drive the skills and capability development agenda across engineering, partnering with engineering directors to upskill teams in prompt engineering, AI-native development patterns, and responsible AI tooling practices.

• Establish guardrails, security controls, and governance frameworks that allow teams to move fast with AI tooling without introducing risk to code quality, IP, or data security.

• Drive and report on engineering AI maturity progression using Keyloop’s established AI maturity framework, setting stage-based targets, tracking adoption across teams, closing capability gaps, and accelerating teams from early experimentation to production-grade AI-augmented delivery.

6.  Delivery Excellence & Observability

• 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), giving engineering teams clear visibility into platform health and performance.

• Drive FinOps discipline across platform infrastructure, owning cloud cost governance and optimisation in partnership with the infrastructure leadership.

7.  Strategic Collaboration

• Collaborate closely with the CTO, engineering directors, architects, and product leadership to align platform capabilities with business priorities and product roadmaps.

• Act as a strategic partner to the broader engineering organisation, ensuring platform decisions accelerate rather than constrain product delivery teams.

• Represent platform and data engineering at the executive and board level, communicating strategy, progress, and investment cases clearly to technical and non-technical stakeholders.

Required Skills & Experience:

Technical & Architectural

• 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.

Leadership & Delivery

• 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.

Agentic Engineering & AI Tooling

• 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.

Communication & Stakeholder Management

• 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.