IT Manager – Platform Engineering, Data Science

• Lead, coach, and develop a high-performing team of platform engineers, architects, and technical specialists.
• Define and execute the platform engineering, cloud, and data science roadmap in support of Ferguson's AI and technology strategy.
• Design, build, and optimize scalable, secure, and reliable platform capabilities across Google Cloud Platform (GCP) and hybrid-cloud environments.
• Partner with AI Engineering, Data Science, Product, and Architecture teams to enable the development, deployment, and operation of AI and machine learning solutions.
• Drive Dev Sec Ops , CI/CD, infrastructure automation, and platform reliability standards to improve delivery speed, quality, and operational efficiency.
• Own and enhance MLOps and data science platform capabilities, supporting model development, training, deployment, and monitoring at scale.
• Establish engineering standards, architecture patterns, API strategies, and reusable platform services that accelerate software delivery.
• Lead platform modernization initiatives, including cloud adoption, developer experience improvements, automation, and legacy technology retirement.
• Manage project delivery, budgets, vendor relationships, and team capacity to ensure successful execution of critical initiatives.
• Supervise platform performance, security, availability, and scalability while proactively identifying and mitigating risks.
• Build strong partnerships with business and technology leaders to align platform investments with organizational priorities.
• Stay ahead of emerging cloud, AI, data, and engineering technologies to drive innovation and continuous improvement.

• Bachelor’s degree in information technology, computer science or related field preferred, or equivalent experience.
• At least 5 years of hands‑on experience in platform engineering.
• Experience in machine learning implementation and data analytics enablement is required.
• Broad knowledge of how platform capabilities support integration and innovation across different business domains.
• Prior experience directly leading engineering or technical talent, including performance management and career development for direct reports; experience with offshore/onsite consultants preferred.
• Direct experience in software programming with Java and/or Python, along with Software Architecture and Engineering expertise.
• Experience in secure software delivery (Dev Sec Ops ) and continuous integration/continuous deployment (CI/CD), and pipeline development.
• Hands‑on experience architecting and operating platform services on Google Cloud Platform (GCP) as the primary hyperscaler, with working knowledge of Azure to support hybrid‑cloud workloads and legacy system integration.
• Experience enabling data science and ML workflows using cloud-native tooling (e.g., Vertex AI, Big Query, or equivalent feature‑store, pipeline‑orchestration, and model‑serving constructs) strongly preferred.
• Experience creating operational dashboards, telemetry configurations and alerting templates for end‑to‑end flow of data services using APM and Data Logging solutions such as App Dynamics, Data Dog etc.
• Solid understanding and experience implementing software design patterns and modern standards.
• Hands‑on software engineer able to work alongside a cross‑functional team of software engineers, software architects, data scientists, and software quality engineers as needed.
• Proactive initiative to find opportunities to improve the platform and data science tooling rather than waiting for direction.

Demonstrates expertise in platform engineering, cloud architecture, and machine learning implementation, with a strong focus on driving Dev Sec Ops  practices and optimizing platform capabilities on Google Cloud Platform. Proven ability to lead and develop high-performing teams while managing project delivery and aligning technology investments with business priorities.

• Platform Engineering
• Machine Learning Implementation
• Google Cloud Platform (GCP)
• Dev Sec Ops
• Software Architecture

• Java
• Python
• Software Architecture
• CI/CD
• MLOps
• Data Analytics
• Cloud-Native Tooling
• Software Design Patterns
• Infrastructure Automation
• API Strategies

• Team Leadership
• Coaching
• Performance Management
• Collaboration
• Proactive Initiative

• Cloud Adoption
• Platform Modernization
• Operational Dashboards
• Hybrid-Cloud Environments
• Software Delivery Standards

• Vertex AI
• Big Query
• App Dynamics
• Data Dog
• Telemetry Configurations