IT Data Engineer

The AI Data Engineer will bridge traditional database administration with emerging AI data infrastructure to advance AI and data modernization initiatives. This role combines AI Data Engineering and Integration, designing scalable pipelines, RAG workflows, vectorized models and secure connectors, with Database Administration and Infrastructure duties as the lead administrator responsible for performance, backup and recovery, upgrades and the transition from third party DBA support to an internal capability.

The position also owns Data Governance and Reliability by defining schemas, metadata and data lineage and by aligning pipelines with security and compliance requirements for high availability and disaster recovery. Finally, the role drives Continuous Improvement and Innovation by evaluating emerging technologies, implementing automation and enabling teams to support next generation AI workloads.

Bachelor's degree from an accredited institution in Computer Science, Data Science, Computer Engineering, Information Systems or a related discipline, or five years equivalent experience.

• Minimum of 7 years of experience in database administration or data engineering within complex enterprise environments.
• Advanced knowledge of administering and optimizing enterprise relational and analytical databases (performance tuning, backup/recovery, replication/clustering, and capacity planning).
• Strong technical foundation in SQL, data modeling, and performance optimization.
• Experience designing or supporting data pipelines that feed AI or advanced analytics platforms.
• Familiarity with cloud-based data ecosystems and large-scale data orchestration.
• Must have hands-on experience with retrieval-augmented generation (RAG), vectorization/embeddings, and vector stores (or equivalent AI data modeling).
• Working understanding of vectorization, embeddings, and retrieval-based AI concepts.
• Proficiency in one or more scripting or automation languages (e.g., Python, Power Shell).
• Must have experience leading vendor-to-internal transitions or similar projects, including planning, knowledge transfer, and operationalizing in-house support within defined timelines.
• Proficiency in MS Office applications that may include but are not limited to Excel, Word, SharePoint, PowerPoint, and Outlook.

The AI Data Engineer will bridge traditional database administration with emerging AI data infrastructure to advance AI and data modernization initiatives. This role combines AI Data Engineering and Integration, designing scalable pipelines, RAG workflows, vectorized models and secure connectors, with Database Administration and Infrastructure duties as the lead administrator responsible for performance, backup and recovery, upgrades and the transition from third party DBA support to an internal capability.

The position also owns Data Governance and Reliability by defining schemas, metadata and data lineage and by aligning pipelines with security and compliance requirements for high availability and disaster recovery. Finally, the role drives Continuous Improvement and Innovation by evaluating emerging technologies, implementing automation and enabling teams to support next generation AI workloads.

Bachelor's degree from an accredited institution in Computer Science, Data Science, Computer Engineering, Information Systems or a related discipline, or five years equivalent experience.

• Minimum of 7 years of experience in database administration or data engineering within complex enterprise environments.
• Advanced knowledge of administering and optimizing enterprise relational and analytical databases (performance tuning, backup/recovery, replication/clustering, and capacity planning).
• Strong technical foundation in SQL, data modeling, and performance optimization.
• Experience designing or supporting data pipelines that feed AI or advanced analytics platforms.
• Familiarity with cloud-based data ecosystems and large-scale data orchestration.
• Must have hands-on experience with retrieval-augmented generation (RAG), vectorization/embeddings, and vector stores (or equivalent AI data modeling).
• Working understanding of vectorization, embeddings, and retrieval-based AI concepts.
• Proficiency in one or more scripting or automation languages (e.g., Python, Power Shell).
• Must have experience leading vendor-to-internal transitions or similar projects, including planning, knowledge transfer, and operationalizing in-house support within defined timelines.
• Proficiency in MS Office applications that may include but are not limited to Excel, Word, SharePoint, PowerPoint, and Outlook.

• Must possess and maintain a valid driver's license and a driving record satisfactory to the company and its insurers (for travel).

• Ability to design, build, and operate scalable batch and streaming data…