New opportunity

VP/AVP, Cloud Engineer, Data Platform, Group Technology

DBS Bank Ltd · Singapore

About this role

As a Cloud Engineer (Data & AI Platform) at DBS, you will be responsible for the design, engineering, security, reliability, and operational excellence of enterprise-scale data, analytics, AI, and agentic platforms on Google Cloud Platform (GCP). You will partner with architects, data engineers, data scientists, and security teams to deliver scalable, secure, and resilient platform capabilities for business-critical workloads. Responsibilities include designing and operating scalable GCP platforms, building reusable infrastructure patterns for AI and agentic workloads, and implementing secure integration patterns for AI agents. You will also define identity and access governance, lead technical design discussions, implement cloud-native engineering practices (IaC, CI/CD, DevSecOps), and serve as an L3 escalation point for complex production issues. Requirements include 8+ years of infrastructure or cloud engineering experience, with at least 5 years specifically on GCP. You must have hands-on experience with enterprise-scale GCP solutions, AI/ML/Generative AI platform engineering, and expertise in BigQuery, Dataproc, and containerized workloads (Kubernetes/Docker). Strong knowledge of cloud IAM, security controls, and data governance is mandatory. Preferred qualifications include certifications such as Google Professional Cloud Architect or Data Engineer, experience leading engineering squads, and development proficiency in Python or Java. Experience with event-driven architectures (Kafka) and monitoring tools (OpenTelemetry, Grafana, Prometheus) is also highly valued.

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

Skills for this role

Google Cloud PlatformGCPBigQueryDataprocCloud StorageSparkAvroParquetKubernetesDockerTerraformInfrastructure as CodeCI/CDCloud IAMPythonJavaKafkaOpenTelemetryGrafanaPrometheusPostgreSQLMySQL

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

Enterprise-Scale GCP Platform Engineering
The role requires designing and operating resilient, cost-efficient data and AI platforms on GCP. You must demonstrate the ability to build reusable infrastructure patterns that support production-grade AI/ML and agentic workloads while maintaining high availability.
Security and Identity Governance
A critical priority is implementing robust security controls, including least-privilege IAM, workload identity, and federated access. You must ensure secure integration between AI agents and enterprise data sources, adhering to strict financial-sector governance standards.
Operational Excellence and L3 Support
As an L3 escalation point, you are responsible for troubleshooting complex production issues across Kubernetes, distributed systems, and data pipelines. This involves leading root cause analysis and implementing permanent corrective actions to ensure platform reliability.
Cloud-Native Automation and DevSecOps
You must drive engineering standards through Infrastructure as Code (Terraform) and CI/CD pipelines. The focus is on embedding security and automated testing into the deployment lifecycle to support rapid, secure delivery of platform capabilities.

Likely challenges to think through

Balancing Innovation with Financial Regulation
Operating AI and agentic platforms in a highly regulated banking environment creates a tension between rapid experimentation and strict compliance. Prepare by articulating how you implement policy-driven guardrails that allow for innovation without compromising data security or auditability.
Managing Complex Hybrid-Cloud Integrations
The requirement for hybrid cloud support suggests potential friction between legacy on-premises systems and modern GCP services. Be ready to discuss strategies for maintaining consistent security, identity, and data access patterns across disparate environments.
Scaling AI/Agentic Workloads
Moving AI models and agent frameworks from development to production at scale is technically demanding. Prepare to discuss how you handle model-serving environments, resource orchestration in Kubernetes, and the specific challenges of managing data formats like Avro and Parquet at enterprise scale.

Practice your answers

Technical practice

Designing Secure AI Agent Integrations on GCP

What to coverExplain how you would architect a secure integration between an AI agent and enterprise data sources. Focus on identity propagation, workload identity, and ensuring least-privilege access to sensitive data within BigQuery or Cloud Storage.

Managing Enterprise-Scale Data Platforms with Dataproc

What to coverDiscuss the trade-offs between ephemeral and persistent Dataproc clusters for AI/ML workloads. Address how you optimize Spark job performance and resource utilization while maintaining cost efficiency.

Implementing Infrastructure as Code (IaC) for AI Workloads

What to coverDescribe your approach to using Terraform for managing complex AI platform environments. Explain how you handle state management, modularity, and integration with CI/CD pipelines to ensure consistent deployments.

Troubleshooting Distributed Systems and Kubernetes

What to coverAs an L3 escalation point, how do you approach debugging a performance bottleneck in a containerized Spark application running on GKE? Mention the use of observability tools like OpenTelemetry, Prometheus, or Grafana.

Identity and Access Governance in Hybrid Cloud

What to coverHow do you design identity patterns for non-human entities in a hybrid cloud environment? Discuss the use of workload identity federation and how you enforce fine-grained access controls across different GCP services.

Data Governance and Security Patterns

What to coverExplain how you implement entitlement-aware authorization for data platforms. Address how you ensure compliance with data governance standards when exposing data to AI models or agentic frameworks.

Optimizing Data Formats for Analytics

What to coverCompare the use of Avro and Parquet in the context of BigQuery and Dataproc. Explain when you would choose one over the other based on read/write patterns and schema evolution requirements.

Event-Driven Architectures with Kafka

What to coverDescribe how you would integrate Kafka into a data platform to support real-time AI inference or data ingestion. Discuss handling backpressure, message ordering, and ensuring high availability.

Behavioural practice

Leading Technical Design and Governance

What to coverDescribe a situation where you had to lead a technical design review for a complex platform initiative. How did you balance the need for innovation with the requirement for strict engineering standards and security governance?

Mentoring and Promoting Engineering Best Practices

What to coverProvide an example of how you have mentored junior engineers or improved the engineering culture within your team. Focus on how you encouraged the adoption of DevSecOps or cloud-native practices.

Handling High-Stakes Production Issues

What to coverTell me about a time you acted as the final escalation point for a critical production outage. How did you manage the pressure, lead the root cause analysis, and ensure a permanent fix was implemented?

Collaborating Across Cross-Functional Teams

What to coverDescribe a project where you had to partner with data scientists and security teams to deliver a platform capability. How did you reconcile conflicting requirements to achieve a secure and scalable outcome?

Sharpen your knowledge

Topics to prepare

GCP Identity and Access Governance
Focus on designing least-privilege access for AI services and non-human identities. Exercise: Draft a policy-driven access model using Workload Identity and service account impersonation for a hypothetical agentic platform, ensuring secure machine-to-machine communication. This is critical for the role's requirement to manage secure integration patterns in a regulated financial environment.
Agentic Platform Engineering
Understand the architecture of platforms supporting AI agents, including model-serving environments and integration with enterprise data. Exercise: Map out a reusable infrastructure pattern for deploying a model-serving environment on GCP that connects to BigQuery via secure APIs. This directly addresses the requirement to build scalable platforms for AI and agentic workloads.

Skills to revise

Infrastructure as Code (Terraform)
Demonstrate proficiency in automating GCP resource provisioning. Exercise: Write a Terraform module that deploys a Dataproc cluster with predefined security controls and IAM bindings. This is essential for the role's focus on cloud-native engineering practices and operational excellence.
Kubernetes and Container Orchestration
Focus on managing containerized workloads in production. Exercise: Create a Kubernetes deployment manifest for a Python-based microservice that includes resource limits, liveness probes, and integration with a secret management system. This aligns with the requirement for hands-on experience with containerized workloads in production.
Data Platform Optimization (BigQuery/Spark)
Optimize data processing pipelines for performance and cost. Exercise: Analyze a complex Spark job running on Dataproc and propose optimizations for data partitioning using Parquet or Avro formats to improve query performance in BigQuery. This is vital for the role's responsibility in engineering enterprise-scale data platforms.

Your preparation checklist

Map GCP expertise to enterprise scale
Prepare to discuss your 5+ years of GCP experience by detailing specific projects involving BigQuery, Dataproc, and Kubernetes. Focus on how you managed production-grade, hybrid cloud environments and solved complex scaling or reliability challenges.
Develop an AI platform engineering narrative
Create a walkthrough of how you have built reusable infrastructure patterns for AI/ML or agentic workloads. Be ready to explain your approach to model-serving environments and how you integrated these platforms with enterprise data sources.
Demonstrate security and governance depth
Prepare concrete examples of implementing least-privilege IAM, workload identity, and federated access. Explain your methodology for securing machine-to-machine communication and managing secrets in a highly regulated financial services environment.
Prepare for technical leadership and L3 troubleshooting
Reflect on your experience leading engineering squads or acting as a technical lead. Prepare a case study of a major production incident where you performed root cause analysis and implemented a permanent corrective action using IaC or DevSecOps practices.
Formulate strategic questions for the team
Prepare thoughtful questions regarding the bank's current AI agentic framework, how they balance innovation with strict financial data governance, and the specific challenges they face in scaling their GCP data platform for future business-critical workloads.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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