Top Data Jobs in Singapore

Lead cost management for complex construction projects from initiation through completion. Responsibilities include cost planning, estimating, reporting, forecasting, value engineering, lifecycle costing, budget and risk management, contract administration, invoice coordination, stakeholder engagement, team leadership, client management, and business development. The role oversees multidisciplinary teams, ensures financial and delivery controls, presents cost plans and reports, and develops cost managers while supporting client objectives and business growth.

Design and build scalable cloud-native data pipelines and lakehouse solutions using Spark, PySpark, Apache Iceberg, streaming technologies, Python, shell scripting, and SQL. Modernize legacy workloads, migrate systems to cloud architectures, implement data quality and monitoring automation, and support metadata, lineage, and governance through open catalogue frameworks. Collaborate with analysts, data scientists, and stakeholders to deliver reliable, high-performance data solutions.

Design and build cloud-native data pipelines, lakehouse solutions, and real-time streaming systems using Spark, PySpark, Iceberg, Python, and SQL. Modernize legacy workloads, migrate processing systems to cloud architectures, automate ETL/ELT workflows, and implement data quality, monitoring, governance, metadata, and lineage practices. Collaborate with analysts, data scientists, and stakeholders to deliver reliable, scalable data solutions and troubleshoot pipeline performance and operational issues.

Leads concurrent standard and complex client projects from conception through implementation and operational handoff. Manages scope, budgets, schedules, quality, risks, issues, change control, stakeholder communications, client satisfaction, and escalations. Coordinates project resources and supports presales activities, solution scoping, proposals, client presentations, and services positioning. Requires strong project management, commercial, communication, influencing, problem-solving, and client engagement skills.

The role involves leading BIO initiatives, collaborating with sales teams, enabling digital capabilities, and engaging stakeholders to enhance agent productivity and business outcomes.

Lead data engineering and process-mining initiatives to capture Digital Footprints, generate actionable visualizations and AI-driven optimizations, standardize scalable digital processes across Front-End sites, and track productivity and labor-hour savings via dashboards and cross-functional execution.

Develop end-to-end data science and ML solutions for semiconductor manufacturing to improve yield, quality, cycle time, and reduce cost. Work across the full pipeline from data acquisition and cleansing through model building, deployment, visualization, and collaboration with international engineering teams. Leverage statistical modeling, deep learning, generative AI techniques, and manufacturing domain knowledge to drive high-impact projects.

Design and deploy computer vision and deep learning solutions for manufacturing inspection and monitoring. Extract and clean large datasets, build multimodal AI models, apply MLOps for production deployment, and collaborate with cross-functional teams to translate research into operational systems and documentation.

Apply data science and ML to semiconductor yield and process optimization: build data pipelines, analyze sensor and tool logs, support DOE, develop models, and create dashboards to guide engineering improvements.

Design, build, and maintain scalable data pipelines, data models, feature stores, and data catalogues to support AI model training and inference. Ensure data quality, lineage, governance, and secure integration of diverse real-time and batch transport data sources. Collaborate with AI architects, infrastructure engineers, and stakeholders to deliver production-grade data products and maintain pipeline reliability.

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.

Develop and maintain end-to-end Xceptor data automation and integration solutions for banking and capital markets workflows. Configure MessageHub channels, Mail integrations, and REST/SOAP APIs; build parsing, routing, transformation, reconciliation, validation, and exception-handling rules; integrate financial platforms; troubleshoot production workflows; optimize performance; and collaborate with architects, analysts, and market specialists.

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

The IT Engineer (Data Services) is responsible for managing and optimizing database systems, ensuring data availability, performance, and compliance, while also developing automated solutions and collaborating with site customers.

Lead commercial technology and data initiatives for EMEA commercial teams: manage cross-functional projects, drive adoption of SFA/TPM/CRM tools, ensure compliance (SOX, GDPR), perform business analysis, change management, stakeholder engagement, and SLA/vendor management.

Design, build, deploy, and improve ML- and LLM-powered services for intelligent automation and AI-driven product experiences. Develop reliable Python systems end to end, including monitoring, validation, observability, experimentation, and performance optimization. Partner with Engineering, Product, and Design teams to define metrics, evaluate models, run controlled experiments, analyze results, and ship scalable AI capabilities. Contribute to technical design and code reviews while improving prompting, retrieval, tool use, and orchestration strategies.

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.

Designs and maintains scalable batch and real-time data pipelines, data warehouses, lakes, marts, and APIs. Extracts, cleans, transforms, and integrates data using cloud platforms and big-data frameworks. Develops reliable ETL/ELT solutions, supports backend applications, applies data governance and security practices, and collaborates with architects, analysts, developers, designers, and project managers in an Agile environment.

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.

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.

Analyze semiconductor manufacturing data to identify yield detractors, build data pipelines, apply statistical and machine learning models for yield improvement, support DOE experiments, and create automated dashboards and reports for engineering stakeholders.

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.