Director of Data, AI, & Analytics

About Our Client

A Singapore-headquartered digital payments firm, licensed and regulated by the Monetary Authority of Singapore, operates an integrated platform serving businesses and partners entering the digital asset market. Working alongside an affiliated licensed brokerage and custody entity, the organisation delivers services spanning over-the-counter transactions, fiat payments, digital asset custody, and prime brokerage. It is a regulated, compliance-first environment where data underpins both customer-facing products and enterprise-wide risk and reporting obligations.

About the Role
The Director of Data will own the enterprise data agenda end to end — spanning strategy, database operations, data platform engineering, governance, analytics, and AI enablement. The role calls for a hands-on leader who can translate architectural and governance decisions into measurable business, regulatory, and AI outcomes. Working across Product, Engineering, AI, Operations, Compliance, Security, and Infrastructure, the successful candidate will build and lead a multidisciplinary function that treats data as a governed, reliable, and AI-ready enterprise asset.

Work Arrangement: On-site
Location: Singapore
Industry: Fintech / Digital Payments & Digital Assets

Tech Stack / Tools
AWS PostgreSQL MySQL Oracle RDS/Aurora DynamoDB Redis Snowflake Databricks dbt Airflow Kafka Spark NoSQL ETL/ELT APIs event streaming real-time data processing RAG vector databases embeddings semantic search feature stores knowledge graphs model gateways LLM platforms MLflow AI evaluation frameworks

What You Will Do
Define and drive the enterprise-wide data and AI enablement strategy, target architecture, operating model, and multi-year roadmap.
Establish clear ownership and accountability across database management, data engineering, governance, BI, analytics, and AI data products.
Recruit, develop, and lead a high-performing multidisciplinary data organisation.
Collaborate with Product, Engineering, AI, Operations, Compliance, Security, and Infrastructure stakeholders to align data priorities with business goals.
Prioritise data investments by weighing business value, regulatory obligations, operational risk, and readiness for AI adoption.
Own the architecture, administration, security, reliability, and lifecycle management of production databases.
Set standards for database design, schema management, performance tuning, capacity planning, upgrades, patching, and change control.
Ensure mission-critical databases meet agreed availability, performance, backup, recovery, RTO, and RPO targets.
Implement database monitoring, replication, high availability, disaster recovery, and operational runbooks.
Optimise database performance and cost without weakening reliability or security.
Lead delivery of a secure, scalable, cloud-based data platform.
Establish data warehouse, lakehouse, ETL/ELT, real-time streaming, orchestration, and data-serving capabilities.
Define standards for data modelling, integration, testing, metadata, lineage, documentation, and lifecycle management.
Build reusable, governed data products consumed by applications, analytics, AI models, and agents.
Establish the trusted data foundation required for generative AI, machine learning, predictive analytics, and enterprise agents.
Build governed pipelines for preparing, enriching, labelling, indexing, and serving structured and unstructured data to AI applications.
Enable patterns and technologies including RAG, vector databases, semantic search, embeddings, feature stores, knowledge graphs, and real-time AI data services.
Govern AI datasets across provenance, consent, access, retention, intellectual property, and permitted usage.
Partner with AI and application teams to evaluate model inputs, retrieval quality, grounding, traceability, and output reliability.
Build monitoring for AI data pipelines, knowledge freshness, retrieval accuracy, data drift, and usage.
Establish a consistent enterprise BI framework with trusted metrics, standardised definitions, and governed data models.

What You Need to Succeed
5+ years of experience across database management, data engineering, data platforms, analytics, or AI data, including significant leadership experience.
Proven track record defining and executing enterprise data strategies and building modern cloud data platforms.
Strong experience managing business-critical production databases in highly available environments.
Strong knowledge of relational and NoSQL databases, data architecture, data modelling, performance optimisation, replication, backup, and recovery.
Experience with data warehouses, lakehouses, ETL/ELT, APIs, event streaming, and real-time data processing.
Practical understanding of generative AI, machine learning, RAG, embeddings, vector search, knowledge management, and AI data pipelines.
Experience preparing and governing enterprise data for AI and advanced analytics use cases.
Strong knowledge of data governance, quality, lineage, metadata, security, privacy, retention, and regulatory compliance.
Experience delivering enterprise BI, management reporting, and self-service analytics.
Demonstrated ability to lead cross-functional initiatives and influence senior business and technology stakeholders.
Strong business judgment, structured thinking, communication, and execution skills.
Nice to Have
Experience in fintech, payments, banking, digital assets, or another regulated industry.
Experience managing high-volume transactional and financial data.
Experience with technologies such as AWS, PostgreSQL, MySQL, Oracle, RDS/Aurora, DynamoDB, Redis, Snowflake, Databricks, dbt, Airflow, Kafka, and Spark.
Familiarity with AI and data technologies such as vector databases, knowledge graphs, model gateways, LLM platforms, MLflow, feature stores, and AI evaluation frameworks.
Experience implementing data products for AI agents, intelligent automation, fraud detection, risk management, or customer analy