VP - Lead Data Engineer (Big Data)

Tangspac Consulting Group; Singapore; On-site; SGD 170,000 - 250,000; Full time

Job summary

Tangspac Consulting Group is seeking an experienced Lead Data Engineer for a financial institution in Singapore. You will lead the design and delivery of enterprise-scale data platforms supporting regulatory reporting, risk management, and analytics.

You will champion modern lakehouse architectures, govern unstructured data, and ensure AI outputs are traceable and compliant. Strong seniority is required with hands-on expertise in Databricks, Snowflake, and Python data tools.

Qualifications

• 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.
• Strong hands-on technical expertise with Databricks, Snowflake, and lakehouse architectures.

Responsibilities

• Lead architecture, design, and implementation of enterprise-scale data platforms for banking and regulatory workloads.
• Define target-state data platform architecture using modern lakehouse technologies.
• Drive adoption of lakehouse architectures with Iceberg, Delta Lake, and Hudi.
• Establish engineering standards for scalability, security, observability, and resilience.
• Optimize performance, cost, and operational efficiency across data estates.

Skills

Leadership

Regulatory compliance

Data governance

NLP analytics

Architecture design

Tools

Databricks

Snowflake

Microsoft Fabric

Google BigQuery

Apache Iceberg

Delta Lake

Apache Hudi

Trino/Presto

Hive

SQL

BTEQ

Pandas

NumPy

Job description

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