Manager-Data Platform Program Delivery

Job Description

The role will act as the senior onsite engagement lead for the client, governing end-to-end delivery, operational readiness, release management, platform stability, AI operations, and managed services execution across multiple enterprise platforms.

The role is responsible for designing, launching, and scaling enterprise grade DataOps, MLOps, AI operations, CI/CD, application support, and managed services capabilities from the ground up. This includes enabling governance frameworks, operational standards, release governance processes, service management models, operational KPIs, support structures, and scalable delivery capabilities.

In addition, the role plays a key leadership function in stakeholder management, capability development, business growth, and long-term service expansion. The successful candidate must demonstrate strong leadership across Data & AI delivery, production operations, organizational capability building, and executive stakeholder engagement within complex multi-vendor environments.

Responsibilities

• Enterprise Platform Delivery & Operations Leadership
• Own enterprise delivery and operational governance across multiple data platforms.
• Lead production readiness, hypercare governance, release management, operational acceptance, and transition into managed services.
• Govern enterprise platform lifecycle management including environment management, deployment governance, rollback planning, and operational stabilization.
• Ensure delivery alignment with strategic business objectives, roadmap priorities, operational timelines, and release schedules.
• Drive operational scalability, resiliency, observability, monitoring, and continuous service improvement initiatives.
• DataOps, MLOps & AI Operations
• Establish and scale enterprise-grade DataOps, MLOps, CI/CD, and AI operations frameworks.
• Define operational standards for model deployment, monitoring, retraining coordination, drift management, inference operations, and AI governance controls.
• Govern AI operational readiness and support processes for enterprise AI use cases across multiple production platforms.
• Standardize release management, deployment automation, testing governance, and operational quality assurance processes.
• Managed Services & Service Offering Development
• Design and scale enterprise Data & AI managed services offerings covering platform operations, application support, DataOps, MLOps, and AI operations.
• Develop reusable operational frameworks, governance standards, support models, delivery playbooks, and service catalogs.
• Define L1/L2/L3 support operating models, escalation procedures, SLA structures, and operational KPIs.
• Drive continuous improvement initiatives to enhance service quality, operational maturity, and delivery efficiency.
• Program Management & Delivery Governance
• Lead delivery execution across multiple Data & AI workstreams including data engineering, analytics, governance, AI enablement, and operational support.
• Monitor delivery progress, operational risks, issues, dependencies, and readiness activities across all programs.
• Ensure compliance with enterprise standards, governance policies, cybersecurity requirements, and operational controls.
• Govern delivery reporting, performance tracking, financial oversight, and operational accountability.
• Stakeholder Leadership & Client Engagement
• Act as the senior onsite engagement lead for Data & AI programs and operations.
• Build trusted relationships with executive stakeholders, sector leadership, technical teams, and implementation partners.
• Lead executive governance forums, steering committees, operational reviews, and strategic planning sessions.
• Drive alignment across business, architecture, operations, AI, infrastructure, and governance stakeholders.
• Vendor & Multi-Partner Governance
• Govern multi-vendor delivery and operational coordination across implementation partners, cloud providers, platform vendors, and support teams.
• Manage dependencies, escalation management, operational coordination, and delivery alignment across all involved parties.
• Capability Building, Staffing & People Management
• Define the long-term capability model required for sustainable enterprise Data & AI operations.
• Lead workforce planning, staffing strategy, onboarding, capability development, and succession planning.
• Support recruitment and management of Data & AI delivery, operations, governance, engineering, AI, PMO, and support resources.
• Coach and mentor teams to establish high-performance delivery and operational practices.
• Business Growth & Commercial Support
• Identify service expansion and upsell opportunities aligned with client strategic priorities.
• Support development of proposals, business cases, effort estimates, pricing models, and service expansion strategies.
• Manage delivery financials including forecasting, utilization tracking, operational efficiency, and margin optimization.
• Communication & Executive Reporting
• Provide executive-level dashboards, operational updates, release readiness reports, risk registers, and performance reporting.
• Lead communication planning and change management activities supporting operational adoption and stakeholder engagement.
  
  

Qualifications

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Management, Artificial Intelligence, Engineering, Business Administration, or a related field.
• 12+ years of experience in enterprise Data & AI delivery, platform operations, managed services, or digital transformation programs.
• Proven experience leading enterprise scale DataOps, MLOps, AI operations, or Data & AI platform support organizations.
• Experience managing production critical enterprise platforms and large scale release programs.
• Experience establishing managed services, operational governance models, and scalable support capabilities from inception.
• Experience managing executive stakeholder relationships within government or large enterprise environments is highly preferred.
  
  

Technical Competencies

• Strong understanding of DataOps, MLOps, CI/CD, enterprise data platforms, data architecture, data governance, metadata, lineage, data quality, and analytics ecosystems.
• Experience operationalizing enterprise AI use cases in production environments.
• Strong understanding of AI/ML operationalization including model deployment, monitoring, retraining cycles, drift detection, inference operations, and AI governance controls.
• Familiarity with operational processes including testing governance, deployment pipelines, incident management, problem management, and service operations.
  
  

Leadership & Management Competencies

• Excellent executive stakeholder management, communication, negotiation, and leadership skills.
• Demonstrated experience leading multi-disciplinary teams across delivery, operations, AI, engineering, governance, and support functions.
• Strong strategic thinking and operational leadership capabilities.
• Ability to operate effectively in complex, multi-stakeholder, and multi-vendor environments.
  
  

Mandatory Requirements

• Fluent in English communication skills required, Arabic language fluency is very much preferred.
• Willingness to work onsite and lead client-facing engagement activities.