Tangspac Consulting Group
VP - Data Analytics (ML, Python,Big Data)
VP-level data analytics role (recruited by Tangspac Consulting for a leading bank in Singapore) leading end-to-end delivery of enterprise data, analytics and AI-enabled solutions. Core stack includes Python, SQL, and modern big data platforms such as Databricks, Snowflake, Azure Fabric, BigQuery, Hadoop and Spark.
- Compensation
- Not disclosed
- Location
- Singapore, Singapore
- Work setup
- Not specified
- Seniority
- C level
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data analytics
- Posted
Skills
Must have
- 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
- 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
Full 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.