Enterprise Lakehouse Architect: Data Products & Agentic AI

NTT SINGAPORE PTE. LTD.; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

NTT SINGAPORE PTE. LTD. invites applications for Lead Enterprise Lakehouse Architect to own the end-to-end architecture of a large-scale Lakehouse platform across on-prem, hybrid and cloud, enabling data products, real-time analytics and agentic AI workloads.

This is a senior hands-on role with production implementation expectations, including governance, data contracts, SLAs, and data-quality controls. Onsite from office, 9-month contract term, targeting large-scale data estates.

Qualifications

• 10–15 years of relevant experience in enterprise data architecture, big-data platforms and distributed data processing.
• At least five years of hands-on architecture ownership for enterprise-scale data platforms.
• Personally architected and implemented at least one production-scale Lakehouse in banking or financial services.
• Hands-on implementation experience with platforms: Cloudera, Huawei Cloud, Google BigQuery/Dataplex/Dataproc, AWS EMR, Azure Synapse or Azure Databricks.
• Production implementation of Bronze, Silver and Gold medallion architecture.
• Deep hands-on experience with Delta Lake, Apache Iceberg or Apache Hudi.
• Ability to explain ACID transactions, schema evolution, partition evolution, time travel/snapshots, compaction and small-file management.
• Experience designing distributed Spark/PySpark workloads and performing query, storage and compute optimisation.
• Production experience implementing both batch and real-time/streaming pipelines.
• Hands-on Data-as-a-Service implementation using REST APIs and Kafka/Pub-Sub.
• Experience building reusable foundation and business data products supported by data contracts, SLAs and automated data-quality controls.
• Experience publishing governed data products through a catalogue, exchange or data marketplace.
• Experience with enterprise object storage and hot, warm and cold lifecycle strategies.
• Experience implementing metadata management, data lineage, RBAC, audit logging and fine-grained access controls.
• Production experience enabling RAG workloads using embeddings and a vector database.
• Practical knowledge of graph databases, prompt engineering, context management and LLM governance.
• Experience designing hybrid-cloud platforms, private connectivity, workload placement and egress-cost optimisation.
• Hands-on Infrastructure-as-Code experience using Terraform, CloudFormation or ARM/Bicep.
• Strong CI/CD implementation experience using Jenkins, Azure DevOps, Cloud Build, GitHub Actions or equivalent.
• Experience with platform monitoring, incident management, performance engineering and continuous service improvement.
• Ability to work onsite at IH2, Malaysia throughout the 12-month assignment.

Responsibilities

• Define the technical vision, target architecture and implementation roadmap for an enterprise-scale Lakehouse platform.
• Architect reusable, scalable and secure platform components across on-prem, hybrid and cloud environments.
• Design Bronze, Silver and Gold medallion layers using Delta Lake, Iceberg or Hudi.
• Design object-storage architecture with lifecycle management and data-tiering strategies.
• Architect distributed Spark/PySpark workloads across multiple platforms.
• Establish data products with contracts, SLAs, ownership, lineage and data-quality rules.
• Serve governed data products via REST APIs, Kafka/Pub-Sub, real-time streams and data marketplaces.
• Create reusable patterns for ingestion, processing and retrieval-augmented workloads.
• Enable RAG workloads using embeddings, vector databases and context-management strategies.
• Design secure hybrid-cloud connectivity and data-egress controls.
• Implement Infrastructure-as-Code and automated provisioning.
• Lead platform performance engineering, capacity planning and FinOps initiatives.
• Evaluate technologies via RFPs and PoCs.
• Define functional/non-functional, security and solution-design specs.
• Review designs for architecture/engineering/security compliance.
• Integrate Lakehouse with CI/CD, testing, source-control, monitoring and incident management.
• Lead continuous service-improvement initiatives.

Skills

Enterprise data architecture

Lakehouse platforms

Big data processing

Distributed computing

Spark

Databricks

Cloud platforms

Data contracts & governance

AI workloads & RAG

Infrastructure as Code

Tools

Delta Lake

Apache Iceberg

Apache Hudi

Spark

Databricks

BigQuery

EMR

Synapse

Terraform

Job description

NTT SINGAPORE PTE. LTD. invites applications for Lead Enterprise Lakehouse Architect to own the end-to-end architecture of a large-scale Lakehouse platform across on-prem, hybrid and cloud, enabling data products, real-time analytics and agentic AI workloads.

This is a senior hands-on role with production implementation expectations, including governance, data contracts, SLAs, and data-quality controls. Onsite from office, 9-month contract term, targeting large-scale data estates.