GCB 4

Sr Associate Director, Data & Analytics — Data Delivery Lead / Business Architect (B4, IC)

Organisation: Data Tech Asia & Middle Data Tech Hong Kong
Location: Hong Kong (hybrid)

Role purpose

This B4 individual contributor role is accountable for (1) end-to-end delivery of a portfolio of data products for Hong Kong, (2) acting as a business architecture interface to HK stakeholders to support business growth, and (3) driving simplification and AI adoption using data across Asia & Middle East (AME) markets. The role translates strategy into a prioritised roadmap and drives execution through strong governance, cross-team coordination, and a disciplined risk-and-control mindset.

Key responsibilities

1) Business architecture & stakeholder partnership (HK growth enablement)

• Partner with HK business stakeholders to understand growth priorities and decisioning needs; translate these into data product capabilities and measurable outcomes.
• Shape problem statements, target-state capabilities, value streams, and operating impacts (process, MI, controls).
• Lead demand intake and discovery: clarify requirements, success metrics, benefits, and dependencies; support prioritisation and sequencing.
Maintain traceability from business objectives data capabilities backlog
• delivered outcomes.

2) Data product delivery leadership (portfolio execution)

• Own delivery for multiple data products (e.g., ingestion pipelines, curated datasets, data marts, MI/BI enablement): scope, plan, milestones, dependencies, and release cadence.
• Run delivery governance: RAID, change control, decision logs, and senior stakeholder reporting; escalate early with options and recommendations.
• Coordinate delivery across engineering, platform/DevOps, architecture, testing, cyber, service management, and production support to deliver stable, supportable releases.
• Drive delivery discipline: definition of ready/done, sprint/release planning, environment readiness, test strategy alignment, cutover planning, runbooks, and BAU handover.

3) AME simplification & AI adoption (data-enabled transformation)

• Identify and drive simplification opportunities across AME markets by standardising data products, reducing duplication, and promoting reusable patterns (common data models, shared pipelines, repeatable controls).
• Partner with AME stakeholders to prioritise and deliver AI-enabled use cases that leverage trusted data products (e.g., decision support, automation, insight generation), ensuring clear value measures and adoption plans.
• Establish practical governance for AI/data initiatives: intake, value tracking, model/data readiness, and alignment to risk, privacy, and security requirements.
• Promote best practices and enablement across markets (playbooks, templates, communities of practice) to accelerate delivery and consistent adoption.

Key stakeholders

• Data Tech Asia & Middle leadership; Data Tech Hong Kong Product Owners, Tech Leads, Engineers, Architecture, Platform/DevOps
• AME market technology and business stakeholders consuming data products and AI capabilities
• Data Management / governance partners, Cyber Security, Risk & Compliance
• Service Management / Production Support

Essential skills and experience

• Strong track record delivering data products (ETL/ELT, curated datasets, marts, MI/BI enablement) in a regulated environment.
• Demonstrable capability in business analysis / business architecture: translating strategy into capabilities, roadmaps, requirements, and measurable outcomes.
• Strong delivery management across Agile/hybrid models with robust dependency management and senior-level reporting.
• Solid understanding of data engineering concepts (orchestration, modelling, APIs, batch/streaming, testing, observability).
• Experience supporting simplification/standardisation across multiple markets or business units.
• Working knowledge of AI/analytics delivery lifecycle (use-case framing, data readiness, experimentation to production, adoption), with strong control mindset.
• Excellent stakeholder management; able to influence without authority across geographies.

Preferred

• Exposure to cloud/modern data platforms and AI tooling (e.g., Azure/AWS/GCP; Databricks/Snowflake; MLOps concepts).
• Familiarity with tooling: Jira/Confluence, ServiceNow, MS Project; SQL; Power BI/Tableau.
• Understanding of data governance practices (metadata, lineage, DQ frameworks, access management).

Qualifications

• Bachelor’s degree (or equivalent experience) in a relevant discipline.
• Delivery certification preferred (PMP/PRINCE2/Scrum/SAFe).

Success measures (examples)

• Increased reuse and reduced duplication of data products across AME (fewer bespoke builds, faster delivery through standard patterns).
• Delivery of priority HK data products that enable growth outcomes (adoption, MI availability, improved decisioning).
• AI use cases delivered with measurable value and adoption, with appropriate controls and operational readiness.
• Improved delivery predictability, reduced incidents/defects, and strong audit/control outcomes.

Opening up a world of opportunity

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