About the role
AI summarisedLead the design and implementation of enterprise data platforms, driving data architecture standards, governance, and quality across ingestion, storage, transformation, serving, and consumption layers. This role requires deep technical expertise in building scalable data solutions while aligning with business stakeholders on data strategy and compliance.
IndustrialOnsite
Key Responsibilities
- Design and implement the enterprise data platform across all layers: ingestion, storage, transformation, serving, and consumption.
- Define and drive standards for data modeling, metadata management, lineage tracking, ownership, security, quality, and compliance (including PDPA).
- Drive enterprise data governance outcomes by aligning business owners, DPOs, and IT stakeholders on policies and processes.
- Collaborate with data stewards to define business metadata, maintain glossaries, lineage schemas, and domain structures.
- Design scalable data integration patterns across enterprise applications, operational systems, and analytics environments.
- Develop and maintain trusted, efficient data pipelines, curated datasets, and enterprise data assets.
- Partner with AI Engineers to ensure the data platform supports AI/ML use cases, including feature engineering and reproducible training data.
- Provide insights to business units through advanced analytics and visualization.
- Contribute to roadmap planning, architecture governance, and continuous improvement of the data platform.
Requirements
- Bachelor’s degree in Computer Science, IT, Engineering, Data Science, or related field (equivalent experience considered).
- Minimum 8 years in data architecture/engineering or similar enterprise data roles.
- 5+ years of experience specifically in data governance, management, or data protection roles.
- Proven experience designing enterprise-scale data platforms and integration patterns.
- Solid understanding of enterprise data architecture (Data Warehouse, Data Lake/Lakehouse, Dimensional Modeling).
- Strong knowledge of data cataloging, metadata management, lineage, and data quality.
- Hands-on experience building or guiding implementation of robust data pipelines and engineering solutions.
- Strong communication and stakeholder management skills across technical and business teams.