Data Engineer

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 for intelligent candidate analysis and matching. HummingBird offers robust product capabilities, including AI-generated job descriptions, automated pre-screening interviews, predictive insights, and pre-built and custom analytics reports to support better decision-making. The unified recruitment ecosystem integrates quickly with major ATS/VMS systems, supports social media and QR code job sharing, and provides access to over 20 million global talents. Employers benefit from configurable workflows, customizable scoring models, referral tracking, and white-labeled branding to tailor the candidate journey to their needs.

Role Description The Data Engineer will design, build, and maintain scalable data pipelines and infrastructure that power HummingBird’s AI-driven talent platform. In this full-time hybrid role based in Los Angeles, CA, with some work-from-home flexibility, the Data Engineer will develop and optimize ETL processes, manage data warehousing solutions, and ensure data quality and reliability across multiple data sources and systems. Daily responsibilities include collaborating with product, data science, and engineering teams to model data structures, support analytics and reporting requirements, and improve data performance and security. The role involves monitoring and troubleshooting data workflows, implementing best practices for data governance, and contributing to continuous enhancement of the platform’s data capabilities. The Data Engineer will also support integration projects with external ATS/VMS systems and other third-party tools to maintain a unified, efficient data ecosystem.

Qualifications

• Strong data engineering skills, including experience designing and implementing scalable data pipelines and services.
• Proficiency in data modeling and data warehousing to support analytics, reporting, and AI-driven applications.
• Hands-on experience with Extract Transform Load (ETL) tools and frameworks for integrating and transforming large, complex datasets.
• Ability to perform data analytics, including querying, profiling, and validating data to ensure accuracy and usefulness for stakeholders.
• Experience with modern data stack tools (e.g., cloud data platforms, distributed processing frameworks, SQL and NoSQL databases).
• Knowledge of data governance, security, and compliance best practices in a cloud-based environment.
• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience.
• Effective collaboration and communication skills, with the ability to work in a cross-functional, hybrid environment.
• Experience in HR tech, recruitment platforms, or AI-powered products is a plus.