{"id":1953315,"url":"https://alion.io/job/ocbc-bank-data-engineer-mgr","title":"Data Engineer (Mgr)","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":83,"open_postings":123,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-08T05:49:30Z"}},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":"full_time","work_mode":"on_site","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":37000,"max_usd":74000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Flask","optional":false},{"name":"Flink","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"AI Agents","optional":true},{"name":"ETL/ELT","optional":true},{"name":"GCP","optional":true},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-10-06T11:06:30Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-09T03:18:53Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"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.\nROLE\nWe are seeking a Data Engineer (Manager) to support the development, maintenance, and improvement of data pipelines for enterprise data warehouse and AI knowledge base platforms within a banking environment. Working under the guidance of senior engineers, you will help transform structured and unstructured data into reliable, reusable datasets that support use cases such as risk management, customer engagement, fraud detection, and intelligent automation. This is a hands-on role with strong learning opportunities across cloud, streaming, and AI-enabled data engineering.\nThis role reports to Executive Director, Data Engineering, Group Data Office.\nKEY RESPONSIBILITIES\nBatch & Streaming Data Pipeline Development\nDevelop and support data pipelines feeding data lake/ data warehouse platforms, such as Cloudera, AWS Redshift, Snowflakes, under senior guidance.\nSupport the implementation of data models for reusable analytical datasets and reporting\nMonitor data pipelines against established SLAs and assist in troubleshooting data quality and processing issues\nDevelop and maintain batch processing jobs using Spark, SQL, Python, or Java\nAssist with real-time streaming pipelines using Flink or similar tools\nBuild and support ingestion pipelines from APIs, GA4, files, and other data sources\nSupport messaging and streaming integrations using Pub/Sub or Kafka\nWrite clean, tested, and maintainable SQL and Python code for data ingestion and processing\nOrchestration & Automation\nDevelop and support Airflow DAGs (or similar tools) for scheduled and event-driven workflows\nFollow established orchestration, coding, and operational standards\nCloud Infrastructure & DevOps\nSupport data workloads on Cloudera, or AWS, with exposure to Docker, Kubernetes ((or similar tools)), or Cloud Run\nUse and support established CI/CD pipelines\nDevelop and support simple REST APIs and backend services using Python and Flask\nSupport Redis caching for data services where required\nAI Knowledge Base & RAG Support\nAssist in developing vector database pipelines and embedding generation jobs\nSupport document processing and chunking workflows for AI knowledge bases and RAG pipelines\nHelp test and monitor semantic search and retrieval quality for AI-facing data layers\nundefined\nCross-functional Collaboration\nWork with senior data engineers, AI teams, and business stakeholders across Risk, Finance, Marketing, and Operations\nAssist in translating business requirements into clear technical tasks\nParticipate in code reviews, testing, documentation, and continuous improvement activities\nREQUIREMENTS\nBachelor’s degree in computer science, information systems, engineering, or a related field\nAt least 2 years of relevant experience in data engineering, data platforms, software engineering, or related roles.\nHands-on experience building or supporting data pipelines using SQL and Python in a data warehouse, data lake, or cloud environment.\nBasic knowledge of AI knowledge base concepts, such as vector databases, embeddings, or semantic search, is a plus\nExposure to RAG components, such as document chunking, embedding generation, or retrieval, is a plus\nExposure to data layers supporting LLM or AI agent use cases is a plus.\nExperience or interest in banking and financial services is a plus\nTechnical Stack\nData Warehouse / Platform: basic experience with Cloudera, Redshift, or similar platforms\nBatch Processing: working knowledge of SQL and Python; exposure to Spark, ETL, or MapReduce\nStreaming: exposure to Flink or another real-time processing engine is good to have\nOrchestration: basic experience with Airflow or equivalent tools\nCloud & Infrastructure: exposure to GCP or AWS; basic knowledge of Docker, or Cloud Run\nDevOps / DataOps: basic exposure to CI/CD\nBackend & Serving: Python, Flask, and REST APIs;\nAdditional Preferred Experience\nEagerness to learn data engineering, real-time streaming, cloud, and low-latency system design\nExposure to LLM applications, RAG, or AI agent concepts is welcome but not required\nA quality-focused mindset and willingness to treat data as a reusable product\nGood communication, teamwork, problem-solving, and willingness to learn\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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