Job Summary
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Lead the organization‑wide data foundation for AI by defining the architecture, standards, quality, governance, and security required to make trusted, well‑managed data available at scale for AI/ML and GenAI initiatives. Own data readiness, pipelines, and platform capabilities across business units, ensuring the entire organization can build, deploy, and operationalize AI use cases in a scalable, reliable, and responsible way. |
Job Responsibilities*
1. Data Strategy & Architecture for AI
Define the enterprise AI data architecture and standards (models, integration, storage), align the roadmap with business/AI priorities, and ensure cloud native scalability.
2. Data Readiness for Use Cases
Orchestrate organization‑wide data readiness—source discovery, profiling, quality gating, metadata/lineage—so AI use cases can be built and operated reliably.
3. Build & Maintain Data Pipelines for AI
Own the design and lifecycle of batch/stream pipelines and feature/data products; enforce SLAs/SLOs, observability, and cost/performance guardrails.
4. Data Governance, Quality & Compliance
Establish and enforce policies for data access, privacy (e.g., PDPA/GDPR), retention, and quality; embed controls for responsible AI training/inference.
5. Data Product Ownership
Define product vision, contracts, and service levels for shared data/feature assets; manage backlog and versioning to enable reuse across business units.
6. Technology Evaluation & Platform Integration
Evaluate and integrate platform capabilities (catalog/lineage, lakehouse, streaming, quality, MDM/feature store); drive modernization of legacy estates.
7. Cross-Functional Collaboration:
Lead by expertise to align AI Architects, Program Managers, MLOps/Data Engineering, and Business Data Owners on priorities, dependencies, and release plans.
8. Data KPIs & Performance Monitoring
Define and publish KPIs for availability, quality, usage/adoption, cost, and time‑to‑AI; report value realization and risks to executives and steering forums.
Qualifications
- Bachelor’s or Master’s in IT / Computer Science / Information Systems / Data Science (MBA or PMO/PM background is a plus).
- 8–10+ years in data platform/engineering/architecture with enterprise‑scale delivery; proven ownership of AI data foundation (readiness, pipelines, quality, metadata/lineage) without direct people management.
- Hands‑on experience supporting AI/ML/GenAI data needs (e.g., RAG pipelines, embeddings, feature stores, knowledge indexing) and operationalizing AI use cases across BUs.
- Strong in data architecture & modeling, data engineering, and enterprise data management.
- Deep understanding of data governance/quality, privacy & regulatory compliance (PDPA/GDPR), access controls, retention, and responsible‑AI data controls.
- Broad knowledge of cloud‑native data stacks (DWH/Lake/Lakehouse), ETL/ELT & streaming, catalog/lineage/MDM/feature store, observability, and CI/CD for data.
- Working knowledge of FinOps/performance guardrails for storage/compute is a plus.
- Proven ability to align data initiatives with business outcomes, translate requirements into scalable data products/pipelines, and set platform/AI data standards adopted across BUs.
- Solid project/portfolio orchestration (plan/track/risk/issue) in complex, cross‑functional environments (IT, MLOps, Data Engineering, Security, BU owners).
- Excellent executive‑level communication and stakeholder management; able to lead by expertise (standards/architecture decisions) across business and technical teams.
- Ability to simplify complex data/AI concepts for senior executives and drive organization‑wide adoption.
- Strong analytical thinking: able to diagnose data/platform issues, balance quality–reliability–cost, and derive actionable insights for AI delivery at scale.
- Commitment to staying current with emerging data/AI technologies, methods, and governance practices and to continuously improving platform capability.