Sobre esta vaga de Data Engineer (VP) na OCBC Bank
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)
Role Purpose
The Senior, Modern Data Engineering role at OCBC leads the transformation of data engineering capabilities by developing cloud-native, AI-enabled, and scalable data platforms that support analytics, AI/ML, Agentic AI, and enterprise decision-making. This senior leader drives the modernization of data architectures, engineering practices, and lakehouse platforms to enhance agility, reliability, automation, and operational excellence. They build and mentor a high-performing data engineering team, promote AI-driven engineering and modern best practices, and foster a culture of innovation and continuous improvement. Collaborating closely with Business, Technology, Analytics, and AI teams, the role delivers secure, trusted, and governed data solutions while establishing reusable platforms, standards, and capabilities through a Data Engineering Centre of Excellence.
Key Accountabilities
Lead the design and delivery of scalable, secure, and high-performance enterprise data platforms aligned with Group architecture standards.
Drive modernization of legacy data platforms to cloud-native and lakehouse architectures.
Enable trusted, high-quality, and governed data through strong data management, cataloguing, lineage, and observability practices.
Partner with Business Units, Analytics, AI/ML, and Technology teams to support business growth and innovation.
Establish engineering best practices, automation, and reusable frameworks across the data ecosystem.
Build AI-ready data foundations that support Machine Learning, Generative AI, and advanced analytics use cases.
Contribute to regional data initiatives and capability development through a Centre of Excellence model.
Core Responsibilities
Architect, design, and maintain scalable data platforms and pipelines across hybrid and cloud environments, preferably AWS.
Lead development of batch, micro-batch, and real-time data processing solutions using Spark/PySpark and streaming technologies.
Implement and operate modern lakehouse architectures using Apache Iceberg and open metadata/catalog frameworks.
Drive migration and modernization of legacy data workloads to cloud-native architectures aligned with Group standards.
Build and standardize automated ETL/ELT pipelines, data quality controls, monitoring, observability, and orchestration frameworks.
Implement DataOps and CI/CD practices to improve engineering productivity and platform reliability.
Support data governance, lineage, metadata management, and access controls to enable trusted and discoverable data assets.
Build and support AI-ready data platforms that enable Machine Learning, Generative AI (GenAI), and advanced analytics use cases.
Design and implement data pipelines supporting AI/ML model training, feature engineering, vector search, and Retrieval-Augmented Generation (RAG) architectures.
Collaborate with Data Scientists, AI/ML Engineers, and business teams to deliver scalable and governed data products supporting AI adoption.
Partner with Business Units, Analytics, and regional teams to deliver analytics-ready and AI-ready datasets.
Evaluate and adopt emerging technologies to support advanced analytics, AI/ML, and future data platform capabilities.
Ensure platform stability, scalability, security, performance optimization, and cost efficiency.
Required Skills & Experience
Technical Skills
Strong experience in SQL and large-scale data transformation.
Expertise in Apache Spark and PySpark for distributed data processing.
Proven experience with Apache Iceberg and modern lakehouse architectures.
Strong Python programming skills for data engineering, automation, and platform development.
Experience with dbt, Airflow/Dagster, Git, CI/CD, and DataOps practices.
Working knowledge of Unix/Linux shell scripting.
Strong experience with AWS cloud services, including:
Storage and compute services
Data processing frameworks
Streaming and event ingestion services
Workflow orchestration services
Experience with Kafka, Kinesis, or equivalent streaming platforms.
Familiarity with Hadoop ecosystems, relational databases, data lakes, and data warehouses.
Experience with data governance, lineage, metadata management, and data observability tools.
Experience with Data Virtualization technologies.
Experience supporting analytics, BI, and reporting platforms (e.g., Power BI).
Exposure to containerization technologies such as Docker and Kubernetes.
Experience building and managing data platforms supporting AI/ML and Generative AI workloads.
Understanding of ML data pipelines, feature stores, vector databases, embeddings, and Retrieval-Augmented Generation (RAG) architectures.
Experience processing structured, semi-structured, and unstructured data for AI use cases.
Familiarity with MLOps/DataOps practices and integration with machine learning platforms.
Exposure to AI ecosystem technologies such as Amazon Bedrock, SageMaker, Databricks AI/ML, OpenSearch, LangChain, Pinecone, Weaviate, or equivalent platforms.
Professional Competencies
Strong leadership, stakeholder management, and influencing skills.
Strong analytical thinking and problem-solving capabilities.
Excellent written and verbal communication skills.
Ability to manage multiple priorities in a fast-paced environment.
Strong collaboration skills across business and technology teams.
High learning agility and passion for modern data and AI technologies.
Qualifications
Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or a related discipline.
10+ years of experience in Data Engineering, Data Platforms, Big Data, Cloud Data Engineering, or Enterprise Data Management.
Experience leading large-scale data platform implementations and modernization initiatives.
Experience delivering enterprise-scale cloud, lakehouse, and data transformation programs.
Financial services or banking experience will be an advantage.
Preferred / Added Advantage
AWS, Databricks, or other cloud/data engineering certifications.
Experience with Infrastructure as Code (Terraform, CloudFormation, or equivalent).
Hands-on experience supporting enterprise AI/GenAI initiatives from a data engineering perspective.
Experience implementing architectures supporting Large Language Models (LLMs), vector search, knowledge repositories, and Retrieval-Augmented Generation (RAG).
Knowledge of Data Mesh, Data Products, distributed systems, and large-scale lakehouse platforms.
Experience with MLOps platforms, Feature Stores, and AI governance frameworks.
Exposure to DevOps, DevSecOps, and automated deployment pipelines.
Understanding of Responsible AI, AI data governance, and regulatory considerations within financial services.
Experience leading data engineering teams or technical delivery across multiple markets.
What we offer:
Competitive base salary. A suite of holistic, flexible benefits to suit every lifestyle. Community initiatives. Industry-leading learning and professional development opportunities. Your wellbeing, growth and aspirations are every bit as cared for as the needs of our customers.
