Big 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 capabilities. Product features include AI-generated job descriptions, intelligent candidate analysis, automated pre-screening interviews, and comprehensive analytics and reporting tools. HummingBird offers predictive insights, powerful filtering, strategic candidate segmentation, and seamless integration with major ATS/VMS systems. With access to over 20 million global talents and a white-labeled, highly configurable experience, the platform supports unified, efficient recruitment operations for employers of all sizes.

Role Description The Big Data Engineer will design, build, and maintain scalable data pipelines and architectures that power HummingBird’s AI-driven talent platform. In this full-time hybrid role based in San Diego, CA, the individual will work on ingesting, transforming, and storing large volumes of structured and unstructured data, with flexibility to work from home part of the time. Day-to-day responsibilities include implementing ETL processes, optimizing data workflows, developing and maintaining data warehouses and data lakes, and ensuring data quality, reliability, and performance. The Big Data Engineer will collaborate closely with data science, product, and software development teams to support advanced analytics, candidate scoring models, and real-time reporting. The role also involves monitoring data systems, improving operational efficiency, and contributing to the overall data engineering best practices and standards.

Qualifications

• Strong data engineering skills, including experience designing and managing scalable data architectures and pipelines.
• Hands-on expertise with Extract Transform Load (ETL) processes and tools, focusing on reliable and efficient data ingestion and transformation.
• Knowledge of big data technologies and ecosystems (e.g., Hadoop, Spark, Kafka, or similar frameworks) for handling high-volume, high-velocity data.
• Experience with data warehousing concepts and platforms, including dimensional modeling, performance optimization, and data governance.
• Solid software development skills in languages commonly used for data engineering (such as Python, Java, or Scala), along with version control and testing practices.
• Familiarity with cloud-based data platforms and services (e.g., AWS, Azure, or GCP) and modern data infrastructure tools.
• Proficiency with SQL and at least one NoSQL or columnar database technology.
• Ability to collaborate in cross-functional teams, communicate technical concepts clearly, and work effectively in a hybrid environment.
• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience.