{"id":1364658,"url":"https://alion.io/job/ocbc-bank-data-engineer-vp","title":"Data Engineer (VP)","company":{"id":4187,"name":"OCBC Bank","domain":"ocbc.com","url":"https://alion.io/company/ocbc","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":84,"open_postings":123,"ghost_share":0,"stale_share":0.659,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-07T05:47:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"head","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":60000,"max_usd":142000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":80},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"Git","optional":false},{"name":"Hadoop","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenSearch","optional":false},{"name":"Pinecone","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Unix","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-07-13T00:00:00Z","employer_posted_date":"2026-07-13","last_verified_at":"2026-10-08T00:03:13Z","board_verified":true,"closed_at":null,"days_open":87,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":87},"description":"WHO WE ARE:\nAs 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.\nToday, 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.\nWe 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.\nYour Opportunity Starts Here.\nData Engineer (VP)\nRole Purpose\nThe 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.\nKey Accountabilities\nLead the design and delivery of scalable, secure, and high-performance enterprise data platforms aligned with Group architecture standards.\n\nDrive modernization of legacy data platforms to cloud-native and lakehouse architectures.\n\nEnable trusted, high-quality, and governed data through strong data management, cataloguing, lineage, and observability practices.\n\nPartner with Business Units, Analytics, AI/ML, and Technology teams to support business growth and innovation.\n\nEstablish engineering best practices, automation, and reusable frameworks across the data ecosystem.\n\nBuild AI-ready data foundations that support Machine Learning, Generative AI, and advanced analytics use cases.\n\nContribute to regional data initiatives and capability development through a Centre of Excellence model.\n\nCore Responsibilities\nArchitect, design, and maintain scalable data platforms and pipelines across hybrid and cloud environments, preferably AWS.\n\nLead development of batch, micro-batch, and real-time data processing solutions using Spark/PySpark and streaming technologies.\n\nImplement and operate modern lakehouse architectures using Apache Iceberg and open metadata/catalog frameworks.\n\nDrive migration and modernization of legacy data workloads to cloud-native architectures aligned with Group standards.\n\nBuild and standardize automated ETL/ELT pipelines, data quality controls, monitoring, observability, and orchestration frameworks.\n\nImplement DataOps and CI/CD practices to improve engineering productivity and platform reliability.\n\nSupport data governance, lineage, metadata management, and access controls to enable trusted and discoverable data assets.\n\nBuild and support AI-ready data platforms that enable Machine Learning, Generative AI (GenAI), and advanced analytics use cases.\n\nDesign and implement data pipelines supporting AI/ML model training, feature engineering, vector search, and Retrieval-Augmented Generation (RAG) architectures.\n\nCollaborate with Data Scientists, AI/ML Engineers, and business teams to deliver scalable and governed data products supporting AI adoption.\n\nPartner with Business Units, Analytics, and regional teams to deliver analytics-ready and AI-ready datasets.\n\nEvaluate and adopt emerging technologies to support advanced analytics, AI/ML, and future data platform capabilities.\n\nEnsure platform stability, scalability, security, performance optimization, and cost efficiency.\n\nRequired Skills & Experience\nTechnical Skills\nStrong experience in SQL and large-scale data transformation.\n\nExpertise in Apache Spark and PySpark for distributed data processing.\n\nProven experience with Apache Iceberg and modern lakehouse architectures.\n\nStrong Python programming skills for data engineering, automation, and platform development.\n\nExperience with dbt, Airflow/Dagster, Git, CI/CD, and DataOps practices.\n\nWorking knowledge of Unix/Linux shell scripting.\n\nStrong experience with AWS cloud services, including:\nStorage and compute services\n\nData processing frameworks\n\nStreaming and event ingestion services\n\nWorkflow orchestration services\n\nExperience with Kafka, Kinesis, or equivalent streaming platforms.\n\nFamiliarity with Hadoop ecosystems, relational databases, data lakes, and data warehouses.\n\nExperience with data governance, lineage, metadata management, and data observability tools.\n\nExperience with Data Virtualization technologies.\n\nExperience supporting analytics, BI, and reporting platforms (e.g., Power BI).\n\nExposure to containerization technologies such as Docker and Kubernetes.\n\nExperience building and managing data platforms supporting AI/ML and Generative AI workloads.\n\nUnderstanding of ML data pipelines, feature stores, vector databases, embeddings, and Retrieval-Augmented Generation (RAG) architectures.\n\nExperience processing structured, semi-structured, and unstructured data for AI use cases.\n\nFamiliarity with MLOps/DataOps practices and integration with machine learning platforms.\n\nExposure to AI ecosystem technologies such as Amazon Bedrock, SageMaker, Databricks AI/ML, OpenSearch, LangChain, Pinecone, Weaviate, or equivalent platforms.\n\nProfessional Competencies\nStrong leadership, stakeholder management, and influencing skills.\n\nStrong analytical thinking and problem-solving capabilities.\n\nExcellent written and verbal communication skills.\n\nAbility to manage multiple priorities in a fast-paced environment.\n\nStrong collaboration skills across business and technology teams.\n\nHigh learning agility and passion for modern data and AI technologies.\n\nQualifications\nBachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or a related discipline.\n\n10+ years of experience in Data Engineering, Data Platforms, Big Data, Cloud Data Engineering, or Enterprise Data Management.\n\nExperience leading large-scale data platform implementations and modernization initiatives.\n\nExperience delivering enterprise-scale cloud, lakehouse, and data transformation programs.\n\nFinancial services or banking experience will be an advantage.\n\nPreferred / Added Advantage\nAWS, Databricks, or other cloud/data engineering certifications.\n\nExperience with Infrastructure as Code (Terraform, CloudFormation, or equivalent).\n\nHands-on experience supporting enterprise AI/GenAI initiatives from a data engineering perspective.\n\nExperience implementing architectures supporting Large Language Models (LLMs), vector search, knowledge repositories, and Retrieval-Augmented Generation (RAG).\n\nKnowledge of Data Mesh, Data Products, distributed systems, and large-scale lakehouse platforms.\n\nExperience with MLOps platforms, Feature Stores, and AI governance frameworks.\n\nExposure to DevOps, DevSecOps, and automated deployment pipelines.\n\nUnderstanding of Responsible AI, AI data governance, and regulatory considerations within financial services.\n\nExperience leading data engineering teams or technical delivery across multiple markets.\n\nWhat we offer:\nCompetitive base salary. A suite of holistic, flexible benefits to suit every lifestyle. Community initiatives. Industry-leading learning and professional development opportunities. 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