IT Data Engineer

IT Data Engineer – Tallgrass

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, retrieval‑augmented generation (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.

• Design, build, and maintain scalable data pipelines connecting enterprise systems to AI and analytics platforms.
• Develop retrieval‑augmented generation (RAG) workflows and vectorized data models to improve AI information access.
• Build and maintain connectors and APIs for secure, high‑performance data retrieval across on‑prem and cloud environments.
• Orchestrate large‑scale data movement using cloud data platforms to ensure availability for AI and business applications.
• Monitor and optimize data flows for consistency, scalability, latency, and data integrity across the ecosystem.
• Serve as lead administrator for enterprise databases, overseeing performance, clustering, backup, and recovery.
• Manage database upgrades, tuning, capacity planning, and storage optimization for transactional and analytical workloads.
• Plan and execute the transition from external DBA vendor support to internal management within 12 months.
• Implement and enforce database security controls, patching, access management, and encryption standards.
• Support application integrations, data migrations, and deployment of new data environments with minimal disruption.
• Define and maintain data models, schema standards, and metadata to support analytics and AI use cases.
• Collaborate with security, compliance, and governance teams to ensure pipelines and databases meet corporate and regulatory requirements.
• Document data lineage, architecture diagrams, interfaces, and operational runbooks; keep documentation current.
• Apply and maintain best practices for high availability, disaster recovery, and change management.
• Evaluate emerging technologies in data engineering, vector search, and graph‑based relationships and recommend adoption where appropriate.
• Recommend and implement process automation to reduce manual database and integration tasks and improve operational efficiency.
• Support ongoing AI and data modernization strategies by ensuring infrastructure, pipelines, and models are production‑ready for next‑generation workloads.
• Troubleshoot production incidents, perform root‑cause analysis, and implement corrective actions to prevent recurrence.
• Provide guidance, mentoring, and knowledge transfer to operations and development teams to improve reliability and performance.
• Track and report key operational metrics and continuously drive improvements to meet SLA and business objectives.
• Collaborate with a variety of people with tact, courtesy, and professionalism.
• Maintain regular, dependable attendance and a high level of performance.
• Maintain a high regard for personal safety, the safety of company assets and employees, and the general public.
• Other daily, weekly, monthly, or special projects may be assigned.

• Bachelor’s degree in Computer Science, Data Science, Computer Engineering, Information Systems or a related discipline, or five years of 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…