Lead Enterprise Lakehouse Architect – Data Products & Agentic AI

About the role

We are looking for a senior Enterprise Lakehouse Architect to lead the design and delivery of a large‑scale Lakehouse platform that powers governed data products, Data‑as‑a‑Service, real‑time analytics and agentic AI workloads. The role is hands‑on and requires end‑to‑end ownership of architecture, implementation and operational excellence.

Key responsibilities

• Define the technical vision, target architecture and roadmap for an enterprise‑scale Lakehouse platform across on‑prem, hybrid and cloud environments.
• Architect reusable, secure components such as Bronze, Silver and Gold medallion layers using Delta Lake, Apache Iceberg or Apache Hudi.
• Design and optimise distributed compute workloads on Spark, Databricks, BigQuery, EMR, Synapse or equivalent.
• Establish data contracts, SLAs, lineage and quality rules for foundation and business data products.
• Expose governed data products via REST APIs, Kafka/Pub‑Sub, real‑time streams and data‑marketplace interfaces.
• Enable Retrieval‑Augmented Generation and agentic AI workloads using embeddings, vector and graph databases, and prompt‑engineering techniques.
• Implement Infrastructure‑as‑Code (Terraform, CloudFormation, ARM/Bicep) and CI/CD pipelines (Jenkins, Azure DevOps, GitHub Actions).
• Lead performance engineering, capacity planning, reliability improvements and FinOps initiatives.

Required profile

• 10‑15 years of enterprise data architecture and big‑data platform experience.
• At least five years of hands‑on ownership of an enterprise‑scale data platform.
• Successfully designed and delivered a production‑grade Lakehouse in banking or financial services.
• Professional certifications in a major cloud platform (Google, AWS or Azure) and a Databricks or CDMP certification.
• Willingness to work onsite in Singapore for the full contract duration.

Required skills

• Delta Lake, Apache Iceberg, Apache Hudi.
• Apache Spark / PySpark, Databricks, Google BigQuery, AWS EMR, Azure Synapse.
• Kafka, Pub‑Sub, REST API design for Data‑as‑a‑Service.
• Terraform, CloudFormation, ARM/Bicep for IaC.
• Jenkins, Azure DevOps, GitHub Actions for CI/CD.
• Vector databases (e.g., Pinecone, Weaviate) and graph databases (e.g., Neo4j).
• Kubernetes, Helm, Kustomize for container orchestration.