Data Lakehouse Architecture (Banking, 1-year renewable contract)

Dear Applicant,

If you or someone you know is interested, please send the CV directly to quynh.nguyen@evolutionjobs.sg (most preferred, as I may overlook some CVs due to the high volume).

Please note that visa sponsorship is not available at this time.

Key Responsibilities

Own the end-to-end architecture and technical vision of an enterprise Lakehouse platform.

Design and implement scalable data products, data marketplace, knowledge layers, and platforms supporting agentic workloads.

Define target architectures for applications and platforms, with emphasis on reusability, scalability, performance, security, and operational efficiency.

Develop and maintain technical roadmaps and architecture strategies for the Lakehouse platform.

Establish technical frameworks and reusable patterns to accelerate the operationalisation of:

• Unstructured and multimodal content extraction.
• Lambda architecture and deployment patterns.
• Retrieval-Augmented Generation (RAG) and retrieval-augmented data patterns.
• Vector and graph-based data capabilities.
• Agentic workloads and AI-driven data solutions.

Design and implement large-scale distributed and MPP compute workloads across on-premise, hybrid, and cloud environments.

Architect and optimise Lakehouse platforms using open table formats, object storage, data federation, and multimodal query engines.

Design hybrid and cloud architectures using private connectivity, workload placement strategies, Infrastructure-as-Code, and cloud cost optimisation.

Design data contracts, SLAs, data quality rules, and governance standards for foundation and business data products in partnership with business stakeholders.

Enable data products to be consumed by downstream applications through APIs, publish-subscribe mechanisms, generative BI, real-time dashboards, and data marketplaces.

Support the architecture and implementation of RAG, embedding strategies, vector databases, graph databases, prompt engineering, and context management for agentic workloads.

Provide technical quality assurance and ensure delivery conforms to defined software development methodologies, engineering standards, and technology practices.

Review design specifications and technical deliverables produced by development teams.

Create and maintain functional and non-functional specifications, architecture/design documents, deployment guides, and training materials.

Independently install, customise, configure, and integrate software packages and technology solutions.

Participate in RFPs, proof-of-concepts (POCs), and technology/product selection activities.

Drive performance engineering, capacity planning, tuning, and optimisation of data platforms and workloads.

Partner with business stakeholders, technology teams, vendors, and other technology functions to design and deliver enterprise solutions.

Support continuous service improvement, process improvement, and operational excellence initiatives.

Ensure effective integration with DevOps, CI/CD, monitoring, testing, and engineering toolchains.

Provide technical guidance and mentorship while maintaining a high standard of quality across architecture and engineering deliveries.

Key Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, or equivalent experience.

10–15 years of experience in Data Architecture, Big Data, Data Engineering, Data Lake, or Lakehouse implementations.

Strong experience designing and implementing enterprise-scale Data Lakehouse platforms, preferably within the financial services industry.

Hands-on experience with one or more major data/cloud platforms, such as Databricks, Snowflake, Cloudera, AWS, Azure, GCP, Huawei Cloud, or Alibaba Cloud.

Strong experience with large-scale Lakehouse architecture, implementation, performance optimisation, and distributed computing.

Deep knowledge of open table formats such as Apache Iceberg, Apache Hudi, and Delta Lake.

Strong experience with object storage architecture, including hot, warm, and cold data tiering strategies.

Experience with data federation technologies such as Trino, Denodo, and Dremio.

Experience with multimodal/distributed query engines such as Hive, Impala, Apache Kudu, or equivalent technologies.

Proven experience designing MPP and distributed compute workloads across on-premise, hybrid, and cloud environments.

Strong understanding of hybrid and cloud architecture, including private connectivity technologies such as Direct Connect and ExpressRoute.

Experience with workload placement, cloud architecture, egress cost optimisation, and Infrastructure-as-Code.

Strong experience building and serving foundation and business data products through APIs, publish-subscribe/event-driven architectures, real-time dashboards, BI platforms, and data marketplaces.

Experience supporting AI and agentic workloads, including:

• Retrieval-Augmented Generation (RAG).
• Embedding strategies.
• Vector databases.
• Graph databases.
• Prompt engineering.
• Context management.
• Agentic orchestration and workflows.

Strong knowledge of modern vector database technologies, such as Databricks Vector Search, Azure AI Search, Pinecone, ChromaDB, Weaviate, or Snowflake Cortex.

Knowledge of graph databases such as Neo4j, JanusGraph, TigerGraph, Cosmos DB, Amazon Neptune, or equivalent.