New opportunity

AVP, Data Engineer, UOB Asset Management Business Technology

UOB Asset Management · Singapore

About this role

The Assistant Vice President (AVP) for Data Engineering within UOB Asset Management (UOBAM) is responsible for developing and maintaining infrastructure for enterprise data platforms and machine learning initiatives. The role involves collaborating with business stakeholders to translate requirements into effective data visualizations and reporting solutions, while designing and maintaining scalable dashboards using BI tools. Responsibilities include implementing data governance frameworks, managing data warehousing, and building robust data pipelines for ingestion, processing, and transformation. The candidate will ensure data quality, timeliness, and consistency through rigorous validation processes. Required Qualifications: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. At least 5 years of experience in data engineering, business intelligence, or machine learning engineering in a production environment. Hands-on proficiency in Python and SQL. Strong understanding of software development best practices, including version control (Git), testing, and CI/CD. Experience with big data technologies and cloud platforms (GCP, AWS, BigQuery, Kafka). Familiarity with data governance frameworks (e.g., DAMA-DMBOK) and regulatory compliance (e.g., GDPR, CCPA). Experience in DataOps and MLOps best practices. Preferred Qualifications: Experience with additional programming languages such as Java, Scala, or C++. Relevant cloud and data certifications are considered a plus. The role requires excellent communication skills, strong analytical and troubleshooting abilities, and the capacity to work both independently and collaboratively within a multi-disciplinary team.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLData EngineeringMachine Learning EngineeringDataOpsMLOpsBigQueryKafkaGCPAWSPower BITableauLookerData GovernanceETL/ELTGitCI/CDData Visualization

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Scalable Data Infrastructure and Pipeline Development
The role requires designing and maintaining robust data platforms and pipelines for ingestion, processing, and transformation. You must demonstrate the ability to build scalable solutions using cloud-native technologies like GCP, AWS, BigQuery, and Kafka to support enterprise-level data needs.
Implementation of Data Governance and Regulatory Compliance
You are expected to integrate data governance frameworks (e.g., DAMA-DMBOK) and ensure adherence to regulatory standards like GDPR and CCPA. This involves managing data lineage, cataloging, and access controls within a highly regulated financial services environment.
Stakeholder Collaboration and BI Delivery
A core priority is translating complex business requirements into actionable data visualizations. You must be proficient in BI tools like Power BI, Tableau, or Looker to create interactive dashboards that provide clear insights to investment professionals.
Operational Excellence via DataOps and MLOps
You must apply software engineering best practices, including Git, CI/CD, and automated testing, to maintain production-grade data and machine learning systems. Prioritizing reliability and consistency through DataOps and MLOps is essential for long-term platform stability.

Likely challenges to think through

Balancing Rapid Innovation with Strict Financial Governance
In an asset management context, the need for agile data platform development often conflicts with rigid regulatory and data quality requirements. Prepare by discussing how you have successfully implemented automated validation processes that satisfy compliance without stifling the speed of data delivery.
Managing Diverse Stakeholder Expectations
Translating technical data infrastructure capabilities into business-friendly visualizations for investment professionals can be difficult. Prepare by highlighting examples where you successfully bridged the gap between technical complexity and business utility, ensuring that dashboards directly support investment decision-making.
Maintaining System Reliability in a High-Volume Environment
The role demands proactive troubleshooting to prevent issues from snowballing. Prepare by describing your approach to monitoring, alerting, and incident response in production environments, specifically how you identify and resolve bottlenecks in large-scale data pipelines before they impact downstream reporting.

Practice your answers

Technical practice

Designing Scalable Data Pipelines

What to coverDescribe your approach to building robust data pipelines for ingestion and transformation. Focus on how you ensure data quality, timeliness, and consistency, and discuss how you handle failures or bottlenecks in a production environment.

Data Governance and Regulatory Compliance

What to coverExplain how you implement data governance frameworks like DAMA-DMBOK in a cloud environment. Discuss your experience managing data lineage, cataloging, and access controls while ensuring compliance with regulations such as GDPR or CCPA.

Implementing DataOps and MLOps Best Practices

What to coverHow do you integrate DataOps and MLOps into your development lifecycle? Address how you manage version control, automated testing, and CI/CD pipelines to maintain production-grade machine learning and data infrastructure.

Cloud Data Warehousing Strategy

What to coverCompare the trade-offs between different cloud-native data warehousing solutions (e.g., BigQuery vs. AWS services). Explain how you design storage and processing architectures to support large-scale asset management data.

BI Tool Selection and Dashboard Design

What to coverWhen collaborating with stakeholders to translate requirements into visualizations, how do you decide between tools like Power BI, Tableau, or Looker? Discuss your process for ensuring dashboards are both scalable and actionable for business users.

Troubleshooting Data Ingestion Issues

What to coverDescribe a scenario where you had to identify and resolve a critical issue in a data pipeline before it impacted downstream reporting. Focus on your analytical process and how you prevent similar issues from recurring.

Big Data Technologies and Kafka

What to coverDiscuss your experience using Kafka or similar streaming technologies for real-time data ingestion. Explain how you manage message ordering, schema evolution, and backpressure in a high-volume data environment.

Behavioural practice

Collaborating with Non-Technical Stakeholders

What to coverProvide an example of a time you worked with business stakeholders to define data requirements. How did you ensure their needs were translated into effective technical solutions while managing expectations regarding scope and timelines?

Working in a Multi-Disciplinary Team

What to coverDescribe a situation where you had to collaborate with a diverse team of engineers or data scientists to deliver a project. How did you handle conflicting technical opinions or prioritize tasks to meet project goals?

Adapting to New Technologies

What to coverThe role requires evaluating and integrating new data technologies. Share an example of how you researched, tested, and implemented a new tool or framework to improve existing data platform capabilities.

Managing Independent and Collaborative Work

What to coverThis role requires the ability to work both independently and as part of a team. Give an example of how you balance individual deep-work tasks with the need to share knowledge and support team members in a busy environment.

Sharpen your knowledge

Topics to prepare

Data Governance and Regulatory Compliance
Review DAMA-DMBOK frameworks and understand how they apply to financial regulations like GDPR and CCPA. Exercise: Map a hypothetical data lineage for a financial asset dataset and identify where PII (Personally Identifiable Information) requires specific access controls.
DataOps and MLOps Lifecycle
Understand the integration of CI/CD pipelines with machine learning model deployment and data quality monitoring. Exercise: Design a conceptual workflow that triggers an automated data validation test whenever a new dataset is ingested into a cloud warehouse.

Skills to revise

Cloud Data Pipeline Development
Proficiency in building scalable ingestion and transformation pipelines using GCP or AWS services. Exercise: Build a sample ETL pipeline using Python to ingest a CSV file into BigQuery, incorporating a schema validation step to ensure data consistency.
BI Dashboard Design
Creating interactive visualizations to translate complex business requirements into actionable insights. Exercise: Create a mock dashboard in Power BI or Tableau that tracks asset management performance metrics, focusing on clarity and user-driven filtering.
Software Engineering Best Practices
Applying version control and testing to data engineering projects. Exercise: Refactor a legacy SQL script into a modular Python function, implement unit tests for the transformation logic, and commit the changes to a Git repository.

Your preparation checklist

Prepare a technical project walkthrough focusing on DataOps and MLOps
Select a past project where you built a production-grade data pipeline. Be prepared to explain how you integrated CI/CD, version control (Git), and automated testing to ensure data quality and reliability, specifically highlighting your experience with cloud platforms like GCP or AWS.
Review data governance and regulatory compliance frameworks
Refresh your knowledge on DAMA-DMBOK and regulatory standards like GDPR and CCPA. Prepare to discuss how you have implemented data lineage, cataloging, and access controls in previous roles to maintain data integrity and security.
Develop a case study on translating business requirements into BI solutions
Prepare an example of how you collaborated with non-technical stakeholders to gather requirements and translated them into actionable dashboards using tools like Power BI, Tableau, or Looker. Focus on the impact your visualization had on business decision-making.
Formulate targeted questions for the interviewers
Prepare questions regarding the current data infrastructure at UOB Asset Management, such as how the team balances MLOps workflows with existing data governance policies or the specific cloud-native tools currently prioritized for data cataloging and lineage.
Refine your technical proficiency in Python and SQL for large-scale data
Practice solving complex data transformation and ingestion problems using Python and SQL. Be ready to discuss how you have optimized pipelines for big data technologies like Kafka or BigQuery to ensure timeliness and consistency.

Availability last checked: 19 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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