Data Engineer

WHO WE ARE As Singapore's longest‑established bank, OCBC has supported individuals and businesses in achieving their aspirations since 1932. We are transforming into a future‑ready learning organization - leveraging technology and innovation while staying true to our ambition to be Asia's leading financial services partner for a sustainable future. Join us to build the bank of the future, work in collaborative teams, and create lasting value for our customers and communities. ROLE We are seeking a Data Engineer (VP) to design, build, and scale enterprise‑grade data pipelines and platforms within a banking environment. This role owns the end‑to‑end architecture of batch and real‑time data pipelines, AI knowledge base, sets engineering standards for the team, and works closely with the data leaders to turn data into scalable, reusable, and AI‑ready products. You will play a hands‑on technical leadership role - architecting solutions, writing production‑grade code, and mentoring other engineers - across use cases such as risk management, customer engagement, fraud detection, and intelligent automation. This role reports to Head of Data Product, Group data office. KEY RESPONSIBILITIES Data Platform Architecture Own and continuously optimize scalable data warehouse / lakehouse architectures across data platforms (e.g., Cloudera, AWS or GCP)

Design and evolve modern architecture patterns for batch and streaming data pipelines

Define and enforce data modeling, partitioning, and performance‑optimization standards and best practices

Batch & Streaming Data Processing and Orchestration Build and optimize large‑scale batch pipelines using Spark, SQL, Python, Map/Reduce.

Design and implement real‑time streaming pipelines using Kafka, Flink, or equivalent engines

Architect and maintain CDC pipelines using Debezium, Confluent, or Fivetran for real‑time data synchronization

Design and optimize complex Airflow DAGs for end‑to‑end orchestration

Drive standardization of orchestration patterns and reusable components across teams

Cloud Infrastructure & DevOps Architect design and deliver on cloud‑native data infrastructure i.e., Cloudera, AWS, GCP (Docker, Kubernetes, Cloud Run or equivalent)

Own CI/CD pipelines for data engineering workloads and use infrastructure‑as‑code practices, i.e., Terraform.

Design and build REST APIs and backend services using Python / Flask to serve data products

Design and implement caching strategies using Redis to support low‑latency, high‑throughput access

AI Knowledge Base Design solutions for vector databases and embedding pipelines to power semantic search and knowledge bases for AI agents

Architect design experience, including chunking, embedding generation, indexing, and retrieval strategies

Design and build tool‑ready, contextual data layers that LLMs and AI agents can query and reason over

Ensure online/offline consistency and freshness of knowledge base content feeding AI applications

Cross‑functional Collaboration & Mentorship Partner with the data team leaders, AI teams, Infra/SRE team, and business stakeholders

Translate business needs into scalable, production‑ready data products

Mentor mid‑level and junior data engineers review code and uphold engineering best practices

Drive continuous improvement of data engineering standards, tooling, and processes

REQUIREMENTS Bachelor's or Master's degree in computer science or a related field

At least 10 years of experience in data engineering, data platforms, or related roles, including experience leading pipeline design and delivery

Strong understanding of modern data architectures including Data Warehouse, Data Lake, Lakehouse, and batch/streaming systems

Experience building tool‑ready APIs and contextual data layers for LLM / AI agent consumption is preferred

Hands‑on experience owning production data platforms end‑to‑end, including on‑call/reliability ownership

Exposure to LLM applications, RAG architectures, vector databases, or AI agent / tool‑calling frameworks is a plus

Strong product mindset: ability to treat data as a product, not just a project

Ability to abstract complex data problems into scalable solutions

Excellent communication skills across technical and business stakeholders, with demonstrated ability to mentor others

Experience in banking or financial services is preferred

Technical Stack Data Warehouse / Platform: Cloudera, BigQuery, Redshift, Teradata, or similar

Batch Processing: Spark, SQL, ETL, Python, Map/Reduce

Streaming: Flink or other real‑time data processing engines

CDC: Debezium, Confluent, Fivetran, or similar

Data Ingestion: APIs, GA4, Pub/Sub, Kafka, Python pipelines

Orchestration: Airflow or equivalents

Cloud & Infrastructure: GCP (Docker, Kubernetes, Cloud Run) or AWS equivalents

DevOps / DataOps: CI/CD pipelines, Terraform or equivalents

Backend & Serving: Python, Flask, REST APIs, Redis

AI Knowledge Base: RAG pipelines end‑to‑end (OCR, chunking, embedding, indexing, tuning, etc)

Experience building tool‑ready APIs and contextual data layers for LLM / AI agent consumption is strongly preferred

WHAT WE OFFER Competitive base salary and comprehensive benefits.

Strong learning and development opportunities.

Exposure to impactful, enterprise‑scale data and AI initiatives across the OCBC Group.

A collaborative environment that values innovation, craftsmanship, and continuous improvement.

Your wellbeing, growth, and aspirations matter to us as much as delivering value to our customers.