Top Data Jobs in Singapore

Lead the design and delivery of enterprise-scale data platforms and pipelines, using modern data platforms, processing frameworks, streaming technologies, and cloud services. Build, lead, and mentor a data engineering team; support client relationships, proposals, migrations, governance, security, and compliance. Contribute technical thought leadership through blogs and community engagement while delivering solutions from proof of concept through commercialization.

Manages and expands public-sector client accounts by building relationships, ensuring satisfaction, reducing churn, and driving revenue and margin growth. Develops account strategies, identifies opportunities, coordinates global stakeholders and partners, supports clients throughout the buying lifecycle, and manages pipelines, forecasts, and quotas in Salesforce.com. Serves as the primary client contact, resolves issues, and translates business needs into tailored technology solutions.

As a Data Scientist, you will analyze large datasets, develop predictive models, and improve existing tools while collaborating with teams for data analysis projects.

A 12-month graduate programme offering technology and AI/data science tracks. Associates may work in application development, DevOps, cybersecurity, data engineering, AI/ML, infrastructure, cloud, or advanced analytics. Responsibilities may include building GenAI and agentic AI solutions, developing ML models, applying MLOps, governing responsible AI, and collaborating with technical and business teams.

Leads end-to-end data delivery for complex consulting and custom research engagements across ASEAN and North Asia. Responsibilities include data sourcing, enrichment, modelling, validation, governance, quality control, client-ready delivery, stakeholder collaboration, and translating complex data into actionable insights. The role also drives AI, GenAI, automation, process improvement, coaching, and best-practice development.

Manage, maintain, and optimize critical database systems (SQL Server, Oracle). Perform installs, patches, backups, access control, monitoring, performance tuning, incident/change/problem management, on-call support, capacity planning, automation, reporting, and collaborate with global teams and site stakeholders.

Lead rapid discovery and innovation engagements to diagnose enterprise pain points, design platform-driven services, monetize platform capabilities, onboard partners, run discovery workshops, produce BRDs and roadmaps, and align cross-functional stakeholders to deliver ROI-driven digital solutions.

Lead Enterprise AI initiatives by designing, developing, and deploying AI/GenAI solutions. Partner with data engineers, technologists and business stakeholders to prototype, integrate, validate and benchmark LLM and advanced-model applications, manage expectations, and translate analytic results into business outcomes.

Lead end-to-end delivery of enterprise data and analytics solutions for retail banking, spanning requirements analysis, solution design, testing, implementation, governance, and production support for ML/NLP-enabled and rule-based decisioning systems.

Lead techno-functional data solutions for Wholesale Banking, Retail, and Global Markets. Gather requirements, design/validate data models (Teradata FSDM, dimensional), profile and map data from core systems, author specifications, collaborate with architects and engineers, execute data validation tests, and provide data-driven insights for strategic decisions.

Lead enterprise data product and Data-as-a-Service strategy, delivery, operationalization, governance, and adoption within a regulated banking environment. Build reusable, governed data products supporting analytics, AI, reporting, and business decision-making. Establish operating models, metadata and lineage standards, quality controls, self-service consumption channels, and Centre of Excellence practices. Partner with senior stakeholders, engineering, technology, governance, risk, and business teams to prioritize use cases, manage delivery, enable AI context, and track measurable value.

Design and build enterprise-scale ingestion frameworks and Lakehouse platforms. Develop reusable Spark/Python components, data models, data products, quality controls, and observability. Support CI/CD, DevOps, Kubernetes-based deployments, and operationalize ML models for AI-driven analytics and risk/fraud use cases in banking.

Lead regional AFC/AML analytics capability: design, build, validate and deploy AML/fraud detection models; guide country analytics teams; ensure model governance, data suitability, and production readiness across stakeholders and regions.

Lead functional business analysis for enterprise KYC/AML capabilities across Group Technology & Ops. Manage stakeholder relationships, define capabilities, translate business requirements into functional and non-functional requirements, oversee testing and scope control, support deployments and post-go-live knowledge transfer, and lead and develop a team of functional business analysts.

Lead the development and deployment of Generative AI applications, collaborate with teams to deliver business use cases, and enhance data science skills.

Leads Binance’s market data function for its Traditional Finance expansion. Owns strategy, sourcing, licensing, vendor negotiations, integration, governance, entitlements, data quality, lineage, compliance, and budget management. Partners with product, engineering, legal, finance, exchanges, and vendors to build scalable market data infrastructure. Builds and mentors a team while serving as the internal expert on market data and market structure.

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.

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.

Lead technical strategy with channel partners across ASEAN to design, validate, and scale Microsoft Cloud and Data & AI solutions. Drive offer development, run workshops and POCs, enable specializations, and translate technical work into repeatable, revenue-generating partner services aligned to Microsoft Cloud and AI priorities.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Supports APAC data governance and data quality management activities across banking operations. Responsibilities include maintaining governance documentation, coordinating working groups, monitoring and analyzing data quality, investigating exceptions, tracking remediation, preparing management reports, and engaging regional and global stakeholders. The role also contributes to process improvement, reporting automation, testing, and AI-enabled initiatives.

Own analytical quality for a global manufacturing Control Tower, translating operational data into KPIs, forecasts, scenarios, early-warning signals, performance narratives, and recommendations. Frame complex decisions, apply statistics, forecasting, machine learning, and AI, validate models, monitor performance and data quality, and lead analytical workstreams with cross-functional stakeholders. Build prototypes using Python, SQL, R, BI tools, and cloud technologies while communicating insights and limitations to senior leaders.

Drive new business and expand existing accounts by defining sales strategies, developing pipeline (cold calling, email campaigns), managing end-to-end sales processes, coordinating vendors and inventory, maintaining sales reporting and forecasting, and sustaining vendor and partner relationships.

Develop Green AI technologies focused on efficient machine learning, large language models, model optimization, parameter-efficient fine-tuning, retrieval-augmented generation, prompt engineering, and efficient inference. Conduct experiments, benchmark algorithms, analyze performance, contribute to research publications, and collaborate with multidisciplinary teams to translate research into deployable sustainable computing solutions.