Data Engineer (VP)

WHO WE ARE: As Singapore’s longest established bank, we have been dedicated to enabling individuals and businesses to achieve their aspirations since 1932. How? By taking the time to truly understand people. From there, we provide support, services, solutions, and career paths that meet their individual needs and desires. Today, we’re on a journey of transformation. Leveraging technology and creativity to become a future-ready learning organisation. But for all that change, our strategic ambition is consistently clear and bold, which is to be Asia’s leading financial services partner for a sustainable future. We invite you to build the bank of the future. Innovate the way we deliver financial services. Work in friendly, supportive teams. Build lasting value in your community. Help people grow their assets, business, and investments. Take your learning as far as you can. Or simply enjoy a vibrant, future-ready career. Your Opportunity Starts Here. Data Engineer (VP) 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…