VP - Data Analytics (ML, Python,Big Data)

TANGSPAC CONSULTING PTE LTD; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Leading Bank seeks a VP - Data Analytics to drive enterprise data, analytics, and AI-enabled solutions across the organization. You will oversee requirements gathering, design scalable data architectures, and guide production delivery while ensuring regulatory compliance and data integrity.

You will collaborate with business, technology, and data science teams to translate regulatory needs into practical roadmaps and modern data platforms, including ML and NLP capabilities, to deliver measurable

Qualifications

• Proven experience delivering large-scale data, analytics, and decision-support solutions within banking or financial services.
• Strong expertise in business analysis, requirements management, solution design, data modelling, data governance, and regulatory compliance.
• Hands-on experience with modern data platforms such as Databricks, Snowflake, Azure Fabric, BigQuery, Hadoop, Spark.
• Proficiency in SQL and data analytics technologies, with working knowledge of Python and BI/visualization tools such as Power BI or QlikSense.
• Strong understanding of machine learning, NLP, AI governance, explainability, data lineage, and regulatory controls for AI-enabled solutions.
• Experience with data integration, data quality management, metadata management, data lineage, reconciliation, and enterprise data architecture.

Responsibilities

• Lead end-to-end delivery of enterprise data, analytics, and AI-enabled solutions across the full SDLC, from requirements gathering through implementation and production support.
• Analyze business and regulatory requirements and translate them into scalable data architectures, functional designs, and solution roadmaps
• Design and govern modern data platforms supporting structured, semi-structured, and unstructured data, including advanced analytics, machine learning, NLP, and real-time insights.
• Collaborate with business, technology, data science, and operations teams to ensure solution quality, data integrity, regulatory compliance, and successful delivery.
• Drive innovation, platform modernization, data governance, root cause analysis, and continuous improvement initiatives to enhance business value and operational efficiency.

Skills

Data analytics

Regulatory compliance

Business analysis

Solution design

Data governance

AI governance

Stakeholder management

Regulatory controls for AI

Tools

Databricks

Snowflake

Azure Fabric

BigQuery

Hadoop

Spark

SQL

Python

Power BI

QlikSense

Job description

Join a leading bank as a VP - Data Analytics, where you will drive the delivery of enterprise data, analytics, and AI-enabled solutions that support business growth, regulatory compliance, risk management, and customer insights across the organization.

• Lead end-to-end delivery of enterprise data, analytics, and AI-enabled solutions across the full SDLC, from requirements gathering through implementation and production support.
• Analyze business and regulatory requirements and translate them into scalable data architectures, functional designs, and solution roadmaps
• Design and govern modern data platforms supporting structured, semi-structured, and unstructured data, including advanced analytics, machine learning, NLP, and real-time insights.
• Collaborate with business, technology, data science, and operations teams to ensure solution quality, data integrity, regulatory compliance, and successful delivery.
• Drive innovation, platform modernization, data governance, root cause analysis, and continuous improvement initiatives to enhance business value and operational efficiency.

Requirements

• Proven experience delivering large-scale data, analytics, and decision-support solutions within banking or financial services environments.
• Strong expertise in business analysis, requirements management, solution design, data modelling, data governance, and regulatory compliance.
• Hands-on experience with modern data platforms such as Databricks, Snowflake, Azure Fabric, BigQuery, Hadoop, Spark, and related technologies.
• Proficiency in SQL and data analytics technologies, with working knowledge of Python and BI/visualization tools such as Power BI or QlikSense. [
• Strong understanding of machine learning, NLP, AI governance, explainability, data lineage, and regulatory controls for AI-enabled solutions.
• Experience with data integration, data quality management, metadata management, data lineage, reconciliation, and enterprise data architecture.