Lead Enterprise Lakehouse Architect – Data Products & Agentic AI- Contract

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

Lead Enterprise Lakehouse Architect – Open Table Formats, Data Products & Agentic AI

Contract Duration: 09 months

Seniority: L4 – More than 10 years of relevant experience

Working Arrangement: Onsite ( 5 days from office )

Headcount: 1

Role Overview

We are seeking an experienced Enterprise Data Lakehouse Architect to own the end-to-end architecture of a large-scale Lakehouse platform supporting governed data products, Data-as-a-Service, real-time analytics, knowledge layers and agentic AI workloads.

This is a senior hands-on architecture position requiring demonstrable production implementation experience. Applicants whose experience is limited to traditional data warehouses, BI reporting, general cloud architecture or data-engineering delivery without end-to-end Lakehouse ownership will not meet the requirements.

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-premises, hybrid and cloud environments.
• Design and implement Bronze, Silver and Gold medallion layers using Delta Lake, Apache Iceberg or Apache Hudi.
• Design object-storage architecture covering lifecycle management and hot, warm and cold data-tiering strategies.
• Architect MPP and distributed-compute workloads using Spark, Databricks, BigQuery, Dataproc, EMR, Synapse or equivalent platforms.
• Establish foundation and business data products with formal data contracts, SLAs, ownership, lineage and data-quality rules.
• Serve governed data products to downstream applications through REST APIs, Kafka/Pub-Sub, real-time streams, dashboards and data-marketplace capabilities.
• Design reusable patterns for structured and unstructured content ingestion, lambda processing and retrieval-augmented data workloads.
• Enable RAG and agentic AI workloads using embeddings, vector databases, graph databases, prompt engineering and context-management strategies.
• Design secure hybrid-cloud connectivity using private dedicated connectivity, workload-placement strategies and data-egress cost controls.
• Implement Infrastructure-as-Code and automated platform provisioning.
• Lead platform performance engineering, query optimisation, capacity planning, reliability improvements and FinOps initiatives.
• Evaluate Lakehouse, federation, query-engine, vector-database and graph-database technologies through RFPs and proofs of concept.
• Define functional, non-functional, security and solution-design specifications.
• Review technical designs and delivery outputs for compliance with architecture, engineering, security and quality standards.
• Integrate the Lakehouse platform with enterprise CI/CD, testing, source-control, monitoring, scheduling and incident-management tools.
• Lead continuous service-improvement and process-improvement initiatives.

Mandatory Requirements

Applicants must meet all the following requirements:

• Between 10 and 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 at least one approved platform:ClouderaHuawei CloudGoogle BigQuery, BigLake, Dataplex or DataprocAWS EMR or OutpostsAzure Synapse or Azure Databricks
• Production implementation of Bronze, Silver and Gold medallion architecture.
• Deep hands‑on experience with at least one open‑table format: 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.

Mandatory Certifications

Applicants must possess at least two current professional certifications, including:

• One professional‑level cloud architecture or data‑engineering certification from Google Cloud, AWS or Microsoft Azure; and
• One Databricks Data Engineer Professional, Databricks Data Architect, CDMP or equivalent data‑platform certification.

Associate‑level training badges or course‑completion certificates alone will not satisfy this requirement.

Preferred Experience

• Trino, Denodo or Dremio data federation.
• Hive, Impala or Apache Kudu query engines.
• Migration from Teradata, Greenplum or Netezza into a modern Lakehouse.
• Databricks Vector Search, Azure AI Search, Pinecone, Weaviate, ChromaDB or Snowflake Cortex.
• Neo4j, JanusGraph, TigerGraph, Amazon Neptune or Stardog.
• LangGraph, OpenAI Agents SDK, Microsoft Agent Framework, LlamaIndex Workflows or Google ADK.
• Kubernetes or OpenShift deployment using Helm or Kustomize.
• Banking regulatory requirements and controls covering MAS, BCBS 239, AML, data residency and auditability.