AI Data Engineer

Company Description IronCore Data is a provider of secure, scalable, and high-performance data center solutions for businesses of all sizes. The company specializes in colocation, infrastructure hosting, network connectivity, and IT equipment services that support reliable and resilient technology environments. IronCore Data works closely with clients to deliver customized infrastructure solutions that maximize uptime, strengthen security, optimize performance, and enable long-term growth. With a focus on reliability, innovation, and responsive customer service, the team helps organizations deploy mission-critical systems, expand networks, and protect critical assets. IronCore Data is committed to being a flexible, dependable partner for companies looking to confidently scale their operations for the future.Role Description The AI Data Engineer role at IronCore Data is a full-time, on-site position based in Los Angeles, CA. In this role, the AI Data Engineer designs, builds, and maintains data pipelines that support AI and analytics workloads, ensuring data is reliable, secure, and efficiently processed. Day-to-day responsibilities include developing and optimizing ETL processes, implementing data models, managing data warehouse environments, and collaborating with software, infrastructure, and data teams to integrate data systems with AI solutions. The AI Data Engineer monitors data quality, performance, and availability, troubleshoots issues, and implements improvements that enhance scalability and efficiency. The role also involves documenting data architectures, following best practices for security and compliance, and contributing to the continuous improvement of IronCore Data’s data infrastructure to support client needs.Qualifications Candidates should possess strong skills in Data Engineering and Data Warehousing, with experience building and maintaining scalable data pipelines.Candidates should possess skills in Data Modeling and Extract Transform Load (ETL), including designing schemas and developing robust data transformation workflows.Candidates should possess skills in Data Analytics, with the ability to translate data into insights that support AI models and business decision-making.Experience with relational and NoSQL databases, cloud or hybrid data platforms, and distributed data processing frameworks (e.g., Spark, Kafka) is beneficial.Proficiency in programming languages commonly used in data engineering (such as Python, SQL, or Scala) and familiarity with AI/ML data workflows is preferred.Strong problem-solving abilities, attention to data quality, and the capacity to work collaboratively with cross-functional technical teams are important.Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience, is advantageous.