Top Data Jobs in Singapore

Designs, builds, and operates secure AWS-native data pipelines and platforms for batch and event-driven workloads. Develops Airflow workflows using Python and SQL, implements tested data transformations, and integrates enterprise, security, and operational data sources. The role embeds data quality, lineage, observability, governance, encryption, access controls, and monitoring. It also contributes to CI/CD, infrastructure as code, documentation, code reviews, incident analysis, and cross-functional solutions supporting risk and regulatory needs.

Manage commercial real estate site selection and acquisition transactions across APAC from initial scouting through closing. Responsibilities include due diligence, transaction tracking, LOI preparation, contract administration, stakeholder coordination, site studies, permitting, incentives, valuations, reporting, and closing support. The role requires managing complex transactions, dashboards, consultants, legal documentation, and multiple projects while coordinating internal teams, external stakeholders, and government agencies.

Develop quantitative models and data pipelines for MetaBrain decision-intelligence software. Responsibilities include preparing reliable datasets, building forecasting, segmentation, ranking, scoring, or optimization models, validating uncertainty and business value, and operationalizing reproducible analytical workflows with data tests, monitoring, scenario outputs, and explainability.

Prospect and secure new B2B clients for financial data and analytics products across APAC. The role involves qualifying leads, conducting needs assessments, delivering tailored presentations, managing CRM records and pipeline health, collaborating with SDRs, and consistently meeting sales targets. Candidates should have B2B sales experience in data, analytics, or financial technology, strong consultative communication skills, Salesforce experience, and business-level English. Mandarin, Cantonese, or Korean is advantageous.

Develop cloud-native data and AI products, assess emerging AI technologies, build microservices, and implement production-ready algorithms. Responsibilities include collaborating with product, engineering, and industry teams throughout the product lifecycle; deploying AI applications; and ensuring transparency, fairness, scalability, security, and cost effectiveness. The role requires expertise in machine learning, prompt engineering, AI agents, RAG, geometric algorithms, and 3D modeling technologies.

Develop decision-intelligence models and reusable analytical services for market research and advisory use cases. Responsibilities include preparing and validating data, building forecasting, segmentation, scoring, and optimization models, running simulations, quantifying uncertainty, documenting assumptions, and packaging analyses into reproducible notebooks, pipelines, or services. The role also requires monitoring model and data changes, explaining results to advisors, and supporting governed AI-enabled business solutions.

Analyze customer and payment-message screening performance across DBS operating countries. Develop and backtest recommendations for detection rules, matching logic, thresholds, and risk scoring; evaluate risk coverage, false positives, data quality, and operational efficiency. Translate findings into requirements and test scenarios, support UAT and implementation validation, provide regulatory and audit evidence, and advise Compliance, Technology, Operations, and business stakeholders as a screening analytics subject matter expert.

Manage MEP cost management across major construction and data centre projects, including cost plans, estimates, budgets, tendering, procurement support, commercial negotiations, change control, payment applications, forecasts, final accounts, and contract administration. The role provides commercial advice, manages cost risks, supports stakeholder relationships, and contributes to multidisciplinary project delivery and continuous improvement.

Leads cost management and commercial advisory services for mission-critical data centre projects. Responsibilities include cost planning, estimating, procurement strategy, tender evaluation, contract support, post-contract cost control, forecasting, change management, risk assessment, client advisory, stakeholder engagement, and team mentoring. The role requires substantial MEP or CSA expertise, preferably in data centre environments, and experience across pre-contract and post-contract project phases.

Supports cost management for data centre and mission-critical construction projects across pre- and post-contract phases. Responsibilities include preparing estimates, budgets, cost plans and reports; supporting feasibility, value engineering, procurement and tender evaluations; reviewing quotations, payments, variations and final accounts; monitoring project costs; maintaining commercial records; and collaborating with clients, consultants, contractors and project teams. Experience in CSA or MEP packages is encouraged.

Own and grow assigned client accounts by driving new business, expansion, renewals, client satisfaction, and revenue growth. Manage opportunities, pipelines, forecasts, partners, vendors, and internal resources while serving as the primary client contact. Identify client needs, connect them to NTT DATA solutions, develop demand, and support governance, risk, and compliance initiatives.

Lead the design, development, and delivery of AI and GenAI solutions across business domains. Act as an AI Product Owner, translating business challenges into scalable solutions, managing cross-functional delivery squads, and measuring adoption and business value. Develop machine learning, LLM, RAG, agentic AI, orchestration, and governance capabilities while mentoring technical teams and advancing reusable AI standards and assets.

Develop and support batch and streaming data pipelines, data models, ingestion workflows, orchestration, cloud infrastructure, and backend services for banking data platforms. The role also supports AI knowledge bases, vector database pipelines, embeddings, document processing, RAG workflows, and semantic search. Responsibilities include monitoring data quality, troubleshooting pipeline issues, participating in testing and code reviews, and collaborating with engineering, AI, and business teams.

Designs and develops secure, scalable client solutions across cloud and on-premises environments. Responsibilities include gathering requirements, evaluating architectures, creating technical specifications and roadmaps, developing proofs of concept, integrating infrastructure and applications, supporting technical changes, consulting with clients, and contributing to go-to-market services.

Build and maintain production machine learning models and AI products for player personalization, recommendation, propensity scoring, and Next Best Action systems. Collaborate with engineers, analysts, and product stakeholders to translate business needs into pragmatic solutions, integrate models into production, and expand AI-driven player engagement capabilities. Contribute to AI agent architectures, automation workflows, and the roadmap for new ML product opportunities.

Build and maintain ML-ready pipelines for experimental engineering, sensor, and operational data. Responsibilities include feature engineering, data quality monitoring, dataset versioning and lineage, drift detection, data contracts, governance, sensor streaming ingestion, synthetic data infrastructure, and MLOps data-layer components such as feature stores and training dataset registries. The role requires strong Python, SQL, pipeline design, and foundational understanding of ML data lifecycles.

Develop multilingual LLMs for Bahasa Indonesia and regional languages by creating high-quality datasets, performing annotation, preprocessing, model training, evaluation, fine-tuning, and alignment. Ensure data quality, language accuracy, and contextual relevance while collaborating with product and engineering teams to deploy safe, reliable AI features. Improve annotation workflows, document technical solutions, and identify impactful AI use cases.

Manages and grows public-sector client relationships, improves satisfaction and retention, identifies expansion opportunities, and drives revenue and margin targets. Develops account strategies, manages sales pipelines and forecasts in Salesforce, coordinates global teams and partners, addresses client issues, and aligns stakeholders around tailored technology solutions throughout the buying lifecycle.

Leads Vantage’s APAC sustainability strategy, embedding sustainability across development, construction, procurement, operations, and business decisions. Advises executives on ESG risks and opportunities, regulatory and climate issues, stakeholder engagement, customer sustainability needs, and sustainable supply chains. Partners with global sustainability, public policy, communications, sales, and engineering teams to deliver regional programs, reporting, training, data quality, and external engagement. The role requires influencing multinational, cross-functional teams and integrating sustainability into large capital projects.

Develop and deploy deep learning models for computer vision, voice, and text processing. Responsibilities include data preprocessing, model training and optimization, GPU-based deployment, performance evaluation, unit testing, documentation, and production scaling. The role involves translating business requirements into technical solutions, collaborating with engineering teams, and delivering AI systems for KYC and other financial products.

Lead application and systems design, development, and deployment; collaborate with stakeholders to translate business needs into technical solutions; manage and mentor developers; ensure technology standards compliance; and troubleshoot technical issues. The role requires strong application development expertise, leadership, communication, strategic thinking, and customer focus within a banking technology environment.

Conduct technology scans and proof-of-concepts, preprocess data, engineer features, build machine learning models, and present systems to management and users. Potential projects include statistics, machine learning, data visualization, human language technologies, and graph analytics. Interns work closely with mentors to meet project requirements.

Undergraduate data engineering interns will manipulate high-volume streaming data, design databases, conduct technology scans and proof-of-concepts, acquire and query data, develop and test systems, and present solutions. Potential projects include DPI acceleration using Snort and Regex, PCAP search, and KSQL real-time stream processing.

Undergraduate interns will research technologies for efficient big data exploration, then design and build visualization or data-processing components for a Jupyter- and Spark-based analytical platform. The project can emphasize modern frontend development or data science, with opportunities to test solutions at scale.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.