全职 Data Engineer 工作, 薪水, HummingBird - Total Talent Workforce Platform 公司招聘中 - Ricebowl

Company Description HummingBird is a Responsible AI-powered total talent workforce platform that transforms how organizations attract, engage, and retain talent through personalized, data-driven experiences. The platform optimizes every stage of the talent lifecycle—from sourcing and engagement to hiring and retention—using advanced AI capabilities. HummingBird offers features such as AI-generated job descriptions, intelligent candidate analysis, automated pre-screening interviews, and predictive insights to support better hiring decisions. Its unified recruitment ecosystem integrates with major ATS/VMS systems, supports custom analytics and reporting, and provides access to over 20 million global talents. Employers benefit from streamlined operations, enhanced candidate journeys, and tools for strategic talent segmentation, referrals, and social media-enabled job sharing.

Role Description The Data Engineer will design, build, and maintain scalable data pipelines and infrastructure that power HummingBird’s AI-driven talent and analytics capabilities. This full-time, on-site role in Singapore includes developing and optimizing ETL processes, implementing robust data models, and supporting data warehousing solutions that enable reliable reporting and predictive insights. The role involves collaborating with data scientists, product managers, and software engineers to ensure data quality, integrity, and performance across the platform. The Data Engineer will monitor data workflows, troubleshoot issues, improve system efficiency, and contribute to best practices for data governance and security. The position also includes documenting data processes, supporting new integrations, and enhancing analytics capabilities to drive data-informed product decisions.

Qualifications

• Strong Data Engineering skills, including building and maintaining data pipelines and distributed data processing systems.
• Proficiency in Data Modeling to design scalable, efficient schemas that support analytics, reporting, and application needs.
• Experience with Extract Transform Load (ETL) processes, including orchestration, optimization, and automation of data workflows.
• Knowledge of Data Warehousing concepts and tools to support centralized, reliable storage for business intelligence.
• Ability to perform Data Analytics, including querying, aggregating, and interpreting data to support product and business decisions.
• Hands-on experience with SQL and at least one programming language commonly used in data engineering (e.g., Python, Scala, or Java).
• Familiarity with cloud data platforms and services (e.g., AWS, GCP, or Azure) and modern data stack tools is beneficial.
• Understanding of data governance, security, and compliance best practices in a high-volume, data-driven environment.
• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience.
• Collaborative mindset, strong problem-solving skills, and the ability to work effectively in a fast-paced product environment.