About the role

AI summarised

The Data Architect is responsible for designing and implementing an enterprise data platform across all layers, from ingestion to consumption. This role involves defining data standards, ensuring data quality and governance, and enabling AI/ML use cases through reliable data pipelines and curated datasets. The architect collaborates with business, engineering, and infrastructure teams to deliver scalable, secure, and supportable data solutions.

IndustrialOnsite

Key Responsibilities

Design data architecture and implement enterprise data platform across ingestion, storage, transformation, serving, and consumption layers
Define and drive standards for data modelling, metadata, lineage, ownership, security, quality, governance, and compliance (PDPA)
Collaborate with business stakeholders or cross-functional teams to define problems, identify key metrics, and present data-driven recommendations
Design scalable data integration patterns across enterprise applications, operational systems, industrial systems, and analytics environments
Design, develop, and maintain trusted, reusable, and efficient data pipelines, curated datasets, and enterprise data assets
Partner with AI Engineers to ensure the data platform supports AI/ML use cases, including historical data retention, feature-ready datasets, trusted training data, reproducibility, and consistency between training and production usage
Work closely with engineering and infrastructure teams to ensure solutions are practical, scalable, secure, and supportable
Provide insights to business units through advanced analytics and visualization
Contribute to roadmap planning, architecture governance, technology evaluation, and continuous improvement of the enterprise data platform

Requirements

Bachelor’s degree in Computer Science, Information Technology, Computer Engineering, Data Engineering, Information Systems, or a related discipline
Minimum 8 years of experience in data architecture, data engineering, data platform development, or similar enterprise data roles
Experience working with ERP, operational, industrial, or engineering data sources
Exposure to data foundations supporting AI/ML, predictive analytics, or intelligent automation
Strong expertise in data or platform architecture, leading data engineering, or other senior hands-on technical roles
Proven experience designing enterprise-scale data platforms, integration patterns, and analytical data structures
Hands-on experience building or guiding implementation of data pipelines, platform components, and data engineering solutions
Solid understanding of enterprise data architecture, including data warehouse, data lake/lakehouse, dimensional modeling, metadata, lineage, governance, and security
Strong knowledge of data engineering and integration practices such as ETL/ELT, batch processing, CDC, APIs, event-driven integration, schema evolution, observability, idempotency, and reprocessing
Good understanding of data platform requirements for AI/ML workflows, including historical data management, feature engineering, training data preparation, and reproducibility
Strong communication and stakeholder management skills, with the ability to collaborate across architecture, engineering, infrastructure, analytics, and AI teams
Ability to manage multiple projects and deliverables effectively in a dynamic environment
Experience with data warehousing platforms such as Snowflake, BigQuery, Redshift, or Synapse
Experience with data lake/lakehouse ecosystems such as Databricks, Delta Lake, Apache Iceberg, or Hudi
Experience with data integration/orchestration tools such as Airflow, Dagster, dbt, Kafka, or Spark
Familiarity with enterprise databases and integration technologies such as SQL Server, Oracle, PostgreSQL, MySQL, SAP HANA, CDC tools, and API/event-based integration
Experience working with cloud or hybrid environments such as AWS, Azure, or GCP
Exposure to data governance, cataloging, lineage, and data quality tools