Contract - Lakehouse implementation Engineer

Tangspac Consulting Group; Singapore; On-site; SGD 180,000 - 360,000; Full time

Job summary

Tangspac Consulting Group in Singapore is seeking a senior Data Lakehouse Architect to own the end-to-end architecture and technical roadmap for the Lakehouse platform, including data products and a data marketplace.

You will define scalable, secure patterns for RAG, unstructured data, real-time and agentic workloads, partner with business teams on data contracts, and drive continuous improvement with clear documentation.

Qualifications

• 10–15 years of experience implementing Data Lakehouse platforms in financial services.
• Experience with Databricks, Snowflake, Cloudera and major cloud providers (AWS/Azure/GCP).
• Strong knowledge of large-scale distributed data platforms and performance optimisation.
• Experience with RAG and agentic workloads, embeddings, and context management.
• Experience with hybrid/cloud architectures and IaC.
• Experience building data products via APIs, pub/sub, dashboards, and data marketplaces.

Responsibilities

• Own end-to-end architecture and technical roadmap for Lakehouse platform.
• Define target architecture, reusable frameworks, scalability, security, and operationalisation patterns.
• Partner with business and technology teams on data contracts, SLAs, data quality, and solution delivery.
• Ensure delivery meets bank software engineering, quality, and governance standards.
• Lead technology evaluation through RFPs/POCs, software integration, and solution design reviews.
• Drive performance optimisation, continuous improvement, and technical documentation.

Skills

Data architecture leadership

RAG workloads

Lakehouse strategy

Tools

Databricks

Snowflake

Cloudera

AWS

Azure

GCP

Iceberg/Hudi/Delta Lake

Trino

Denodo

Dremio

Terraform

Jenkins

JIRA

Job description

Job description:

• Own theend-to-end architecture and technical roadmapfor the Lakehouse platform, including data products, data marketplace, knowledge layer, and agentic workloads.
• Definetarget architecture, reusable frameworks, scalability, security, performance, and operationalisation patternsfor RAG, unstructured data, real-time and agentic workloads.
• Partner with business and technology teams ondata contracts, SLAs, data quality, and solution delivery.
• Ensure delivery meetsBank software engineering, quality, and governance standards.
• Lead technology evaluation throughRFPs/POCs, software integration, and solution design reviews.
• Driveperformance optimisation, continuous improvement, and technical documentation.

Requirements

• 10–15 years’ experience implementing Data Lakehouse platforms, preferably in Financial Services, using Databricks, Snowflake, Cloudera, AWS, Azure, GCP, or similar.
• Strong experience withlarge-scale distributed data platforms and performance optimisation, including Iceberg/Hudi/Delta Lake, object/tiered storage, Trino/Denodo/Dremio, and distributed query engines.
• Experience designingMPP/distributed workloadsacross on-premise, hybrid, and cloud environments.
• Expertise inRAG and agentic workloads, including embeddings, Vector DB, Graph DB, prompt engineering, and context management.
• Strong knowledge ofhybrid/cloud architecture, private connectivity, workload placement, egress optimisation, and Infrastructure-as-Code.
• Experience building and servingdata productsthrough APIs, pub/sub, real-time dashboards, generative BI, and data marketplaces.
• Experience withDevOps/engineering toolssuch as Jenkins, JIRA, Git, CI/CD pipelines, SonarQube, Terraform, monitoring, Control‑M/Airflow, and testing tools