Lead Enterprise Lakehouse Architect – Data Products & Agentic AI- Contract
# Lead Enterprise Lakehouse Architect – Data Products & Agentic AI- Contract **NTT SINGAPORE PTE. LTD.** · Toa Payoh · `On-site` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Indeed* --- ### About the Role 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. Interested candidates are kindly requested to email their CV with their experience to sandeep.sringeripai@global.ntt We look forward to your application! NTT Singapore Pte Ltd (NTTS) is the regional headquarters of NTT Communications Corporation (NTT Com) for Asia Pacific Region. Established in 1997, NTT Singapore has more than 10 years of expertise in providing information and communications technology (ICT) solutions worldwide. NTT Singapore offers diverse high-quality connectivity, data centre solutions, security services, IT management services, voice and conferencing solutions and solution integration services to its enterprise customers. NTT Communications is a wholly owned subsidiary of Nippon Telegraph and Telephone Corporation (NTT Corp.), one of the world’s largest providers of telecommunications services. In 2013, NTT Corp. is ranked no.1 in telecom industry in the Fortune Global 500* list with operating revenues of more than $133,077 million. It is positioned 32nd among the top 500 corporations worldwide. NTT Com's extensive global infrastructure includes Arcstar secure private networks, which cover 196 countries/regions and a tier-1 IP backbone network connected with major ISPs worldwide, as well as secure data centers at over 150 locations worldwide.
Наблюдалась 2026-09-21, впервые 2026-09-21, источник — Indeed.