Data Engineering Director

About MetaComp

MetaComp Pte Ltd, based in Singapore, is a prominent provider of digital payment solutions, licensed and regulated by the Monetary Authority of Singapore (MAS) as a Major Payment Institution. The company specializes in Digital Payment Token Services and Cross-border Payment Transfers. Operating under a P2B2C model, it offers an integrated suite of services empowering clients to enter the digital asset space with assured safety, security, and regulatory compliance. Alongside its parent company, Metaverse Green Exchange Pte. Ltd., MetaComp delivers various product offerings including over-the-counter transactions, fiat payments, digital asset custody, and prime brokerage through its Client Assets Management Platform (CAMP).

Role Overview

The Director of Data leads the organization’s data strategy encompassing database management, data platforms, governance, analytics, and AI readiness. This leadership role involves crafting a secure, reliable, AI-capable data foundation to support key applications, business intelligence, regulatory demands, product innovations, and enterprise AI agents.

Key Responsibilities

• Develop and implement a company-wide data strategy and AI enablement roadmap including architecture and operational models.
• Assign clear ownership for database management, data engineering, analytics, and AI-enabling data products.
• Build and guide a high-caliber, cross-disciplinary data team.
• Collaborate closely with departments such as Product, Engineering, AI, Operations, Compliance, Security, and IT Infrastructure.
• Prioritize data-related investments balancing business value, compliance obligations, operational risk, and AI preparedness.
• Manage the architecture, security, performance, and maintenance of production databases with set standards for schema, capacity, upgrades, and failover mechanisms.
• Ensure databases meet availability, backup, RTO, and RPO requirements and implement monitoring, replication, and disaster recovery procedures.
• Oversee data platform design including cloud-based warehouses, lakehouses, ETL/ELT processes, real-time streaming, and integration of diverse data sources.
• Deliver governed, reusable data products for application, analytics, and AI consumption.
• Establish a dependable data foundation for generative AI, machine learning, and predictive analytics, including governing data quality and access for AI applications.
• Implement enterprise-wide data governance covering data ownership, classification, privacy, lineage, retention, and compliance, ensuring audit readiness.
• Lead deployment of business intelligence frameworks and self-service analytics to produce timely insights and reduce manual reporting.
• Measure and optimize the business impact, adoption, risk, and cost of AI-enabled data assets.

Candidate Profile

• At least 5 years of leadership experience in database management, data engineering, data platforms, analytics, or AI-related data roles.
• Demonstrated skill in crafting and implementing enterprise data strategies and modern cloud data platforms.
• Deep expertise in managing production-grade, high-availability databases.
• Strong command over relational and NoSQL databases, including architecture, modeling, replication, backup, and performance tuning.
• Proficiency in data warehouse and lakehouse concepts, ETL/ELT, APIs, event streaming, and real-time data handling.
• Practical experience with generative AI technologies, vector search, embeddings, knowledge graphs, and AI data pipelines.
• Thorough understanding of data governance, security, privacy regulations, quality assurance, and metadata management.
• Experience delivering enterprise BI, management dashboards, and enabling AI-driven analytical solutions.
• Ability to lead cross-functional projects and engage senior stakeholders with strong communication and execution skills.

Preferred Qualifications

• Background in fintech, payments, banking, or regulated industries handling complex transactional data.
• Familiarity with AWS, PostgreSQL, MySQL, Oracle, DynamoDB, Redis, Snowflake, Databricks, dbt, Airflow, Kafka, and Spark ecosystems.
• Experience with AI and data technologies including vector databases, knowledge graphs, LLM platforms, MLflow, and feature stores.
• Knowledge of responsible AI practices, model risk management, data privacy, cloud security, and operational resilience.

Success Indicators

• Consistent achievement of database reliability, performance, and security targets.
• Successful establishment of a scalable, AI-ready enterprise data platform.
• Clear data ownership, quality control, access policies, and lineage for critical data.
• Reliable and governed use of data for AI applications and enterprise agents.
• Timely and actionable business reporting and AI-assisted insights.
• Significant reduction in manual data processes through automation and standardization.
• Delivery of measurable improvements in decision-making, customer experience, operational efficiency, risk management, and compliance.

Inclusion Statement

MetaComp is dedicated to creating an inclusive work environment that values diversity across all dimensions including ethnicity, gender, identity, orientation, experience, and thought. We are an equal opportunity employer prohibiting discrimination on any protected characteristic.