Top Data Jobs in Singapore

Gather and analyze business and risk requirements, produce functional designs for Market Risk systems, assess impacts, create test plans, execute UAT/regression/simulation tests, manage enhancement timelines, and provide BAU support including data sourcing, extraction, and analysis for market and liquidity risk data stores.

Lead quote operations and manage tooling and FOB cost data flows. Develop and maintain costing analytics, data pipelines, and dashboards (Power BI/Tableau). Co-lead Databricks analytics capability, support business reviews and margin objectives, drive process improvements, automation, data governance, and embed analytics/AI into costing processes across Product Costing, Manufacturing, Finance, and Technology.

Develop and deploy AI/ML and generative-AI solutions: collaborate with stakeholders, research methods, perform data wrangling and feature engineering, maintain deployed models/assistants, build backend APIs and frontend interfaces, document products and fix security issues.

Design, build, and maintain large-scale batch and real-time data pipelines, ETL/ELT processes, and data stores (data lakes, marts, warehouses). Perform data extraction/cleaning (including web scraping), integrate data silos, develop backend APIs, and collaborate with cross-functional teams in an Agile CI/CD environment.

Lead data science efforts for HMAX: define use cases, build and validate ML/DL models (including time-series and predictive maintenance), industrialize and deploy models, mentor junior engineers, and collaborate with product, domain experts, and software teams to deliver production-ready analytics.

Support cost management for data centre projects including estimating, quantity take-offs, tender support, contract administration, cost tracking, monthly reporting, progress valuations, change control, value engineering, and maintaining audit-ready cost records under guidance of a Cost Manager.

Deliver full lifecycle cost management for Civil, Structural & Architectural (CSA) packages on a large data centre. Produce cost estimates, plans and feasibility studies; advise on design and value engineering; manage tendering, procurement and post-contract cost control including variations, payments, forecasts and final accounts; produce cost reports; identify risks and mitigation; coordinate with project teams, MEP, contractors and clients to ensure financial governance and value optimisation.

Sell Agentforce and Data 360 solutions by identifying and qualifying opportunities, conducting technical discovery, mapping customer needs to Salesforce AI and data capabilities, creating POVs, collaborating with account teams, and driving revenue through technical, enterprise B2B sales.

Design, build, test, deploy and support Xceptor-based automation solutions. Create documentation, reusable components, training materials, and run UAT/SIT. Troubleshoot production, integrate with systems, mentor and onboard team members, and expand capabilities to adjacent automation technologies.

Lead customer analytics and insights to support acquisition, retention, cross-sell, and lifecycle strategies. Build segmentation, propensity models, dashboards, and KPI frameworks; ensure data quality and PDPA compliance; partner with IT, distribution, and business leaders to operationalize insights and support strategic planning.

Develop and implement computer vision and machine learning models (object/change/anomaly detection, classification, behavior analysis, video stitching). Build POCs, run experiments in realistic environments, optimize model robustness, produce documentation and patentable ideas, stay current with AI advances, present findings, and (for senior profiles) provide technical mentorship.

Prepare month-end closes, forecasts, variance analysis, account reconciliations and profitability reviews. Provide insights and recommendations, support budgeting, analyze project and departmental costs, and identify cost-management opportunities for international operations.

Design, develop, and deploy predictive and generative AI models; analyze large datasets; build GenAI-powered applications and reusable AI services; create data visualizations; collaborate with stakeholders to drive business decisions and keep abreast of ML and generative AI advances.

The Growth Data Analyst will create dashboards, analyze data using SQL and Databricks, collaborate with teams, and develop models to enhance marketing strategies.

Lead design and maintenance of scalable data pipelines, ETL/ELT processes, and data warehouse architectures. Optimize infrastructure and data quality, build automated monitoring, support high-priority analyses, and partner with Product, Engineering, and GTM teams to deliver actionable data solutions.

Lead development of enterprise analytic data products, execute data strategies, and communicate technology concepts while guiding and mentoring teams.

Design and develop GenAI solutions for applications and tools, collaborate with teams, optimize AI pipelines, and document workflows.

This role involves designing and developing AIML/GenAI solutions, collaborating with engineering teams, and translating business needs into AI-driven solutions.

The Data Architect will design and maintain data architecture frameworks, create data models, and provide solutions for data architecture. Must drive change management and understand analytics trends.

The role requires managing large Big Data projects, focusing on Hadoop ecosystem architecture and development with specific technologies like Spark, Hive, and Java, among others.

The Big Data role requires expertise in Hadoop, Spark, and various development tools, focusing on Big Data analytics and architecture.

The Data Analytics Developer role requires experience in finance, specifically in banking and financial services, strong data analysis skills, and knowledge of PL/SQL.

The IBM FileNet/Data Cap Administrator is responsible for the installation, configuration, and administration of IBM FileNet and Datacap applications, as well as performing system maintenance and upgrades.

The Data Analyst will work with users and technical teams to implement data warehouse solutions, perform data quality checks, and deliver functional specifications. Banking experience is essential.

The Sr Big Data Engineer will develop data pipelines using big data technologies, manage projects, and implement data modeling for analysis while ensuring efficient querying and performance.