Top Data Jobs in Singapore

The Technical Product Manager is responsible for product outcomes across the lifecycle, involving product vision, stakeholder engagement, backlog prioritization, and technical leadership.

The Transformation Lead for AI in APAC will manage change and drive adoption of AI tools across the studios, engage stakeholders, and run training programs.

The role involves developing technology policies, conducting research, coordinating AI efforts, and engaging with stakeholders to strengthen Singapore's digital economy.

The role involves researching technology policies, analyzing trends, developing strategies for emerging technologies, and coordinating efforts within IMDA for effective policy implementation and stakeholder engagement.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.

The AI Architect will develop AI solutions, support the global AI sandbox, provide technical guidance, and optimize AI products while collaborating with various stakeholders.

The Director of Platform Success is responsible for accelerating platform adoption and business transformation across APAC, ensuring alignment with global strategies while delivering measurable outcomes for regional growth.

As an Enterprise Architect, you will guide major clients in digital transformation and technology roadmap alignment using Salesforce solutions, building relationships and delivering enterprise-level insights.

The Infra Product Manager will develop algorithm platforms and Web3 search systems, integrate AI capabilities, analyze on-chain data, and ensure product delivery quality.

Manage the design and lifecycle of Web3 infrastructure products focusing on data services, market data systems, and algorithm platforms. Collaborate with teams to meet performance and security needs, leveraging advanced AI technologies and on-chain data analytics.

As a Data Engineer, you will design and maintain data infrastructures, build ETL pipelines, collaborate with teams, and ensure data quality.

Design, build, and maintain a unified data framework, scalable data pipelines, and data models. Improve data ingestion, quality, monitoring, and governance; troubleshoot pipeline issues; optimize performance; integrate third-party tools; and apply AI techniques to enhance data quality and engineering workflows.

Senior individual contributor who shapes International Sports Medicine commercial execution using advanced analytics and AI Agents. Develops and owns AI-enabled decision support, dashboards, data infrastructure, automation, and governance. Acts as product owner for AI agents, translates market and account data into actionable insights, leads strategic initiatives, provides market sizing and intelligence, and prepares leadership reporting and KPIs to track execution and corrective actions.

Design, build, and operationalise large-scale cloud data pipelines (batch and streaming) using Databricks/Snowflake and cloud platforms to support reporting, automated systems and AI/ML teams. Administer cloud data platforms, optimise SQL/storage, implement CI/CD, IaC, containerisation and serverless solutions, and collaborate with clients, partners and internal teams to scale data engineering deployments.

A structured graduate programme delivering technical training, mentorship, certifications and hands-on client project experience across cloud, data, AI/ML, application development, automation, infrastructure and cybersecurity to prepare graduates for specialised technology career paths.

The Platforms and Data Engineer will develop and manage high-performance analytics solutions, design data transformation processes, maintain data systems, and support AI projects.

Own and maintain the analytics stack: implement tracking from user interaction to Mixpanel/GA4/AppsFlyer via Segment, manage infrastructure with Terraform, audit pipelines and schemas, translate business needs into tracking, and configure troubleshooting for mobile/web attribution, deep-linking, and postbacks.

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

Assist in developing backend services and APIs for WeChat, build large-scale data pipelines, and contribute to data streaming infrastructure while collaborating with teams.

Lead development and implementation of AI governance frameworks, create playbooks and training, validate standards across business units, industrialise governance tooling, run bank-wide governance processes, and advise stakeholders on responsible AI practices.

Gather and analyze business and risk user requirements; produce functional designs and specifications for market risk systems; assess impacts; create and execute test plans (UAT, regression, simulation); support BAU including data sourcing, extraction, and analysis for Market and Liquidity Risk Data Store; manage delivery timelines.

Lead execution of the department strategic plan, drive cross-team collaboration, implement business strategies, create KPIs and dashboards, manage budgeting and forecasting, oversee contract governance and business continuity, and manage employee experience and day-to-day operations for the Transformation & Data, Group COO unit.

Lead development and maintenance of a bank-wide Data and AI risk management framework. Assess new initiatives, conduct risk reviews, embed controls across data and AI lifecycles, monitor data quality and KRIs, advise business units, align with regulations and industry best practices, and report risks and mitigation strategies to senior management.

Lead development and implementation of enterprise data management standards, frameworks, and tooling. Create playbooks and training, validate unit implementations, drive bank-wide governance processes, engage stakeholders to embed data risk management, and industrialise metadata, lineage, data quality, and accountability across business and technology teams.

Lead operational execution of data quality and governance for risk data. Manage data issue lifecycle, perform profiling and root-cause analysis, implement rules and monitoring, support data stewardship, drive BCBS239 attestation, collaborate with tech and risk units, and optimize tools and processes to ensure data fitness and regulatory compliance.