Role Overview

As a Delivery Manager, you will own the end-to-end delivery of data, analytics and AI engagements — from implementation through to steady-state operations, including service delivery. You will lead project and program delivery, manage scope, schedule, budget, quality and risk, and build lasting client relationships that turn engagements into long-term managed services.

Key Responsibilities

Implementation Delivery

• Lead the planning and execution of data, analytics and AI implementation projects from kick-off to go-live.

• Manage scope, schedule, budget, resources, quality and risks across concurrent workstreams.

• Coordinate consultants, architects, engineers and third-party partners to deliver against milestones and contractual commitments.

• Establish project governance, reporting cadence, change control and stakeholder communication.

Operations & Service Delivery

• Lead the transition from implementation into operations, including handover, runbooks and operational readiness.

• Manage ongoing service delivery against SLAs covering incidents, problems, changes, requests and escalations.

• Operate service governance across monitoring, capacity, availability, security and continuous improvement.

• Own the managed-services relationship — renewals, service reviews, value reporting and account growth.

Client & Team Leadership

• Act as the primary delivery interface for clients; build trust and manage expectations across all levels.

• Lead and develop delivery teams, contractors and partners; foster ownership and accountability.

• Drive DataOps, DevOps and operational excellence practices within engagements.

• Collaborate with practice leads on resource planning, forecasting and account strategy.

Required Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Business Administration or a related discipline.

• 8+ years of experience in IT project/program delivery, managed services or service delivery management.

• Proven track record delivering enterprise data, analytics, cloud or application projects end-to-end.

• Solid understanding of data delivery fundamentals — DataOps, data governance, data quality and data lineage — sufficient to manage data programs end-to-end.

• Strong knowledge of project and service management frameworks, including agile and ITIL practices.

• Excellent client relationship, negotiation, risk and financial management skills.

Preferred Qualifications

• Experience managing data platform, analytics, AI or cloud transformation programs in regulated or complex environments.

• Experience transitioning projects into long-term managed services and growing service accounts.

• Familiarity with data management knowledge areas (e.g. DAMA-DMBOK) plus at least one platform data certification, such as Microsoft Fabric Analytics Engineer (DP-600) or Databricks Data Engineer Associate.

• Domain knowledge of industry regulations and operating requirements for key verticals (e.g. HKMA financial regulations, PDPO data privacy), enabling well-designed SLAs and service contracts.

• Working proficiency in Cantonese or Mandarin is a plus for Hong Kong and Greater China engagements.

What Success Looks Like

• Engagements are delivered on time, on budget and to agreed quality — with high client satisfaction.

• Projects transition seamlessly into operations with minimal friction and stable, well-run services.

• Clients renew and expand their managed services because they trust your delivery leadership.

Working Style

• Collaborate with clients, practice leads, architects, engineers and service delivery teams across the full engagement lifecycle.

• Operate in a consulting-led delivery environment with strong ownership of outcomes.

• Contribute to reusable methodology, accelerators and solution assets for the practice.

• Support enterprise customers in Hong Kong with occasional regional collaboration as required.

Why Join Us

• Join a growing AI Data Intelligence Practice focused on strategy, platform, analytics, AI and managed services.

• Work on enterprise-scale transformation programs with modern data and AI platforms.

• Develop cross-disciplinary capability across consulting, architecture, engineering, data products and adoption.

• Help clients build AI-ready data foundations that support BI, ML, Gen BI and AI agents.

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