VP - Lead Data Engineer (Big Data)
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Summary
A VP-level Lead Data Engineer will head a Singapore bank's enterprise data platform strategy: architecting lakehouse platforms (Databricks, Snowflake, BigQuery, Microsoft Fabric), building NLP pipelines for unstructured data, and enforcing data governance to support regulatory reporting, risk, and AI-enabled analytics.
A Financial Instutition is seeking an experienced Lead Data Engineer to lead the bank's strategic data engineering initiatives and drive the evolution of our enterprise data ecosystem.
This role will be responsible for building and governing modern enterprise data platforms that support regulatory reporting, risk management, commercial banking analytics, operational intelligence, and AI-enabled insights. The successful candidate will lead the design and delivery of scalable data solutions for both structured and unstructured data while ensuring compliance with banking regulations and data governance standards.
Key Responsibilities
Enterprise Data Platform Strategy & Engineering
Lead the architecture, design, and implementation of enterprise-scale data platforms supporting banking, regulatory, and analytical workloads.
Define the bank's target-state data platform architecture leveraging technologies such as:DatabricksSnowflakeMicrosoft FabricGoogle BigQuery
Drive adoption of modern Lakehouse architectures utilizing:Apache IcebergDelta LakeApache Hudi
Establish engineering standards for scalability, availability, security, observability, and resilience.
Optimize performance, cost, and operational efficiency across enterprise data estates.
Unstructured Data & NLP-Enabled Analytics
Lead initiatives to unlock business value from unstructured data sources including documents, customer interactions, regulatory publications, policies, and operational records. Design NLP-enabled data pipelines that support:Document classificationKnowledge extractionText analyticsSemantic searchAutomated summarizationRegulatory intelligence Establish end-to-end frameworks covering data ingestion, preprocessing, model evaluation, explainability, and operational deployment. Ensure AI-generated outputs are traceable, auditable, and suitable for regulatory scrutiny. Establish robust controls for:Data qualityData reconciliationData validationData certification Implement metadata management, lineage, and impact analysis capabilities. Drive the governance and management of Critical Data Elements (CDEs) across business domains. Experience
12+ years of experience in enterprise data engineering, analytics, and data platform delivery. Significant leadership experience within banking, financial services, or other highly regulated industries. Proven track record delivering enterprise-scale data transformation and modernization initiatives. Technical Expertise with Strong hands-on experience with: Databricks, Snowflake Apache Spark, Iceberg, Delta Lake, Apache Hudi Trino / Presto Hive SQL BTEQ Working knowledge of: Pandas NumPy Interested candidates please email your latest resume to (HIDDEN TEXT)
Lead initiatives to unlock business value from unstructured data sources including documents, customer interactions, regulatory publications, policies, and operational records. Design NLP-enabled data pipelines that support:Document classificationKnowledge extractionText analyticsSemantic searchAutomated summarizationRegulatory intelligence Establish end-to-end frameworks covering data ingestion, preprocessing, model evaluation, explainability, and operational deployment. Ensure AI-generated outputs are traceable, auditable, and suitable for regulatory scrutiny. Establish robust controls for:Data qualityData reconciliationData validationData certification Implement metadata management, lineage, and impact analysis capabilities. Drive the governance and management of Critical Data Elements (CDEs) across business domains. Experience
12+ years of experience in enterprise data engineering, analytics, and data platform delivery. Significant leadership experience within banking, financial services, or other highly regulated industries. Proven track record delivering enterprise-scale data transformation and modernization initiatives. Technical Expertise with Strong hands-on experience with: Databricks, Snowflake Apache Spark, Iceberg, Delta Lake, Apache Hudi Trino / Presto Hive SQL BTEQ Working knowledge of: Pandas NumPy Interested candidates please email your latest resume to (HIDDEN TEXT)