Top Data Jobs in Singapore

The Senior/Lead Data Engineer designs and optimizes data solutions on Alibaba Cloud, develops data pipelines, manages data warehouses, and ensures data quality and compliance.

Assist the Group Internal Audit team with digital transformation, process improvement, and analytics use cases for auditing and risk measurement.

Act as a functional analyst for DataMart and CRM, liaising with stakeholders to define requirements and review specifications. Participate in UAT and ensure project timelines are met.

The Market Data Administrator manages key data pipelines, ensures data quality, supports clients, maintains documentation, and contributes to projects for an international expansion.

The Data Science Intern at Agoda will implement ML models, collaborate with engineers, and research new technologies in data analytics.

The Data Analyst I will manage reports, develop dashboards in Power BI, ensure data accuracy, and conduct business analytics for informed decision-making.

The Senior Data Analyst will develop dashboards, analyze business performance, and guide data strategy while maintaining data products and pipelines. Requires 5 years of experience.

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

The Head of PT Data Engineering leads data product management, oversees implementation and adoption of data products, and aligns with Roche's digital strategy.

In this role, you will develop machine learning models, derive insights from large datasets, and collaborate with product and engineering teams to enhance client-facing platforms.

The Senior Data Scientist will lead analytical projects, mentor junior scientists, design experiments, and provide data-driven strategies to enhance Gojek's service offerings.

As a Senior Data Analyst, you will optimize the routing platform, analyze geospatial data, design experiments, and build data products.

Join Razer as a Data Engineer to build data infrastructure for AI and machine learning, focusing on optimization, data pipelines, and collaboration with teams for AI-driven initiatives.

The Sales Manager leads the GIN sales team in APAC, focusing on generating revenue through new and existing customer relationships, supporting sales opportunities, and collaborating with various teams to enhance sales performance.

The Data Engineer is responsible for designing and implementing modern data platforms, developing ETL pipelines, ensuring data quality, and supporting ETL pipeline issue resolution.

Entry-level consultant responsible for research, data analysis, and client deliverables, supporting project execution under guidance.

Lead the Application Solutions division with full P&L accountability, driving growth through client engagement, market strategies, and operational excellence.

Analyze datasets to uncover business opportunities, develop data dashboards, assist in building data pipelines, and collaborate with teams on strategies.

The Technical Specialist leads certification projects, mentors staff, establishes client relationships, and promotes CSA Group services, enhancing customer satisfaction and sales targets.

Design and develop data pipelines using Python, collaborating with teams for data integration and ensuring high-quality code through testing.

The Data Engineer will optimize data infrastructure, develop scalable ETL processes, establish data governance, and collaborate with data teams to ensure data accuracy and performance.

The Software Applications Development Engineer develops and improves applications, tests them, communicates with stakeholders, and ensures alignment with client needs and performance requirements.

Lead the migration of data infrastructure to AWS, redesign Python scripts for AWS services, and create documentation and training for users.

The role involves collecting and analyzing data related to user behavior and product performance, assisting in creating reports and dashboards, and supporting A/B testing efforts.

Lead the migration of data infrastructure to AWS, re-architect Python scripts and automation workflows, and ensure seamless data operations with zero disruption. Create documentation, conduct training, and validate migrated systems.