Top Data Jobs in Singapore

Leads an L2 service recovery team supporting critical banking systems. Oversees major incident resolution, escalations, root cause analysis, service-level adherence, runbooks, post-incident reviews, and continuous improvement. Partners with engineering and infrastructure teams to implement permanent fixes, supports disaster recovery and business continuity activities, ensures regulatory compliance, and maintains 24/7 coverage while developing team capability.

Owns data and AI governance initiatives within a technology unit, including operating-model design, policy and controls adoption, data quality management, assessment reviews, responsible data use, AI governance, risk monitoring, issue remediation, audit readiness, and stakeholder escalation. The role advises data domain leaders, coordinates with enterprise risk and data functions, and strengthens data governance culture across a complex, regulated banking environment.

Designs complex, cross-domain technology solutions spanning applications, infrastructure, cloud, and on-premises environments. Collaborates with clients and internal teams to assess requirements, evaluate architectures, create technical plans, develop roadmaps, deliver proof-of-concept implementations, and resolve problems. Applies enterprise architecture methodologies and standards while mentoring colleagues and supporting consulting engagements.

Designs, builds, deploys, and operates scalable cloud data lakehouse platforms and production ETL/ELT pipelines. Responsibilities include data architecture, open table formats, automated data quality, governance, access controls, CI/CD, infrastructure as code, analytics and AI enablement, documentation, monitoring, incident response, and cost optimization. The role collaborates with technical and business stakeholders across Databricks, Spark, AWS, and Microsoft Fabric environments.

Build and maintain scalable batch and real-time data pipelines for information security and digital risk analytics. Translate business and risk requirements into technical specifications, data models, ETL solutions, and production-ready data products. Support testing, deployment, monitoring, documentation, and production issue resolution while applying data governance, access control, auditability, and secure-by-design practices. Collaborate with business, risk, technology, and platform stakeholders in a regulated banking environment.

Internship data analyst supporting dashboard monitoring, user behavior and content ecosystem analysis, A/B test evaluation, campaign performance analysis, data quality assurance, and metric documentation. The role uses SQL and Excel to derive insights on engagement, retention, conversion, and creator ecosystems, while collaborating with product and operations teams in a blockchain and digital assets environment.

Supports marketing analytics for OCBC Consumer Banking by exploring data, developing predictive models, managing campaign reporting, defining customer metrics, executing targeted campaigns, and supporting segmentation and product initiatives. Partners with business stakeholders, data engineers, and data scientists to develop analytics solutions, recommendation engines, data pipelines, and automated campaign execution that improve customer acquisition, cross-selling, activation, and marketing effectiveness.

Leads global data architecture, governance, master data management, analytics, and business intelligence strategy. Owns data models and roadmaps, ensures data quality and regulatory compliance, prioritizes BI initiatives, and delivers reporting solutions. Identifies automation and process improvement opportunities while supporting digital transformation. Collaborates with stakeholders, application teams, and enterprise architects to translate business needs into scalable solutions and contribute to enterprise architecture planning.

Lead technical strategy for payments optimization to balance conversion, fraud risk, and cost. Design and implement advanced optimization algorithms, guide experimentation and causal inference, collaborate with product and engineering to deploy ML systems at scale, influence product strategy, and mentor junior data scientists.

Lead architecture of a governed, reusable data, knowledge, and skills platform on Databricks to power analytics, AI agents, and real-time decisioning. Design data, semantic, and skills layers, enforce Governance-as-Code, enable regional data residency, and build agent interfaces and APIs while partnering with Data Science, Product, and Risk teams.

Lead architecture, development, and operation of Airwallex's data and AI platforms. Drive company-wide data platform solutions, build scalable multi-region pipelines, enforce governance and security, mentor the team, advocate best practices, and influence technical roadmaps to enable AI/ML-driven business impact.

Design and build scalable batch and streaming ETL pipelines and data models, collaborate with stakeholders to translate data needs, ensure data quality and governance, and own end-to-end delivery for Knowledge Platform initiatives in Singapore.

Lead the Realtime Data Platform team to define a 1–3 year technical roadmap, modernize the core platform for petabyte-scale real-time analytics, partner with product teams to enable data-driven features, scale and structure engineering teams, and serve as a trusted technical advisor across Data and AI initiatives.

Design and build scalable batch and streaming ETL pipelines and data models. Partner with engineers and business stakeholders to deliver reliable SSOT data, ensure data quality, governance, and support ML/AI use cases. Own end-to-end pipeline design, monitoring, and ongoing improvements.

Lead architecture, development, and operation of Airwallex's data and AI platforms. Drive company-wide data platform strategy, build scalable multi-region data pipelines, promote best practices, mentor engineers, and enable data-driven decision making and AI readiness across the organization.

Partner with Product and Engineering to analyze large datasets, run experiments, and apply statistical methods to measure product performance, identify growth opportunities, set goals and forecasts, and drive product strategy through data-driven recommendations.

Lead the development of AI and ML solutions for underwriting, manage a team of data scientists, and ensure compliance and governance in initiatives.

Leads commercial due diligence for data centre leasing and investment decisions. Develops total cost of occupancy models, benchmarks developer and landlord proposals, validates budgets and pricing assumptions, assesses commercial risks, supports negotiations, and prepares investment recommendations. The role advises clients across concurrent transactions, maintaining cost intelligence, reporting, audit trails, and evidence-based commercial strategies from site evaluation through lease execution.

Owns independent schedule assurance for data centre opportunities, challenging developer programmes and validating critical paths, sequencing, durations, resource assumptions, procurement lead times, commissioning, energisation and service-date credibility. Conducts schedule risk analysis, maintains programme assumptions and mitigation strategies, leads stakeholder review sessions, supports commercial negotiations, reports recommendations to executives, and transfers validated programme intelligence to delivery teams.

Leads North American indirect tax requirements and global tax data governance for an SAP S/4HANA transformation. Oversees U.S. and Canadian tax processes, tax determination, reporting, reconciliations, data standards, tax technology integrations, testing, deployment, audit readiness, and compliance. Partners across Tax, Finance, IT, Master Data, Analytics, Procurement, Supply Chain, and Shared Services to improve tax data quality and establish scalable global operating models.

Supports enterprise Data and AI projects through data preparation, ETL pipeline development, dashboard creation, machine learning model testing, database queries, research, documentation, and data validation. Collaborates with data scientists, AI engineers, architects, consultants, and cross-functional teams while participating in agile ceremonies and innovation initiatives. This is a six-month full-time graduate traineeship for candidates without prior work experience.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Intern will evaluate and improve model governance and management practices for predictive maintenance models. They will collaborate with data scientists and equipment experts to pilot an end-to-end ML management framework, document best practices, analyze data, interpret experimental results, and present actionable recommendations supporting operational efficiency and lean manufacturing.

Build and maintain production machine learning models and AI products that improve player retention and engagement. Responsibilities include recommendation engines, segmentation, propensity scoring, Next Best Action systems, AI agent workflows, and personalization solutions. The role requires translating stakeholder needs into pragmatic ML products, collaborating with analysts, engineers, and product teams, and contributing to model deployment, decisioning capabilities, and the AI product roadmap.

Design and build full-stack, cloud-native enterprise applications that manage corporate data from ingestion through serving. Develop APIs, integrations, data pipelines, CI/CD pipelines, observability, and secure data synchronization across networks. Participate in agile sprints, code reviews, documentation, issue resolution, and technical knowledge sharing. The role may also involve integrating LLM and agentic AI capabilities.