Remote AI Data Integration Specialist

Master’s degree or higher in Computer Science, Data Engineering, Information Systems, Computer Engineering, or related technical field (10 years of relevant experience may be substituted) 10+ years professional experience in data engineering, data integration, or ML operations roles Hands-on experience designing and implementing data pipelines for analytics or AI/ML applications Demonstrated experience working with enterprise data integration challenges in complex technical environments Federal government experience, particularly within VA or Department of Defense Strong ML/AI experience with understanding of data requirements for model training, validation, and inference Proficiency in data ingestion and preparation techniques including ETL/ELT pipeline development Experience with data pipeline orchestration tools and frameworks (Azure, Data Factory, or similar) Understanding of metadata tagging standards and data cataloging approaches Knowledge of data classification schemes and minimal data standards for AI/ML readiness Expertise in data source evaluation methodologies including quality assessment and gap analysis Strong understanding of data flows, system integrations, and API-based data exchange patterns Experience with cloud data platforms (Azure preferred) and hybrid cloud/on-premise integration patterns Familiarity with ITSM platforms (ServiceNow preferred) and operational data structures Proficiency in SQL and at least one programming language (Python preferred) for data transformation Expert-level gap analysis capabilities with ability to identify root causes and recommend solutions Strong analytical mindset for assessing data quality, completeness, and fitness for purpose Critical thinking to evaluate trade-offs between data quality, cost, and timeline constraints Systems thinking to understand data dependencies and downstream impacts of integration decisions Ability to calculate and articulate ROI for data initiatives using operational metrics and business value Ability to explain technical data concepts to non-technical stakeholders Strong documentation skills for technical specifications, data flows, and integration patterns Collaborative approach to working with cross-functional teams (SRE, Data Science, Analytics) Experience supporting executive communications with data-driven insights

Conduct comprehensive assessments of existing data sources to determine fitness for AI/ML applications Perform gap analysis identifying data quality issues, completeness problems, and integration challenges Evaluate data source reliability, consistency, and availability for operational AI use cases Document data lineage, dependencies, and transformation logic for governance and auditability Assess technical debt and recommend remediation strategies for data infrastructure improvements Implement metadata tagging standards ensuring discoverability and traceability across data assets Apply data classification schemes aligned with federal security requirements and VA policies Establish and enforce minimal data standards for AI/ML readiness across operational systems Collaborate with Chief AI Office (CAIO) and data governance teams on compliance requirements Design data cataloging approaches that support self-service discovery for analytics and AI teams Support ML model development by preparing training datasets with appropriate feature engineering Build and maintain data infrastructure supporting ML experimentation, training, and deployment Implement data versioning and lineage tracking for ML reproducibility and auditability Calculate and communicate ROI for data integration initiatives, demonstrating value through operational metrics Identify opportunities where improved data integration can accelerate AI adoption or enhance model performance Partner with SREs, Data Scientists, and Analytics teams to understand data requirements and constraints Translate technical data challenges into understandable terms for government stakeholders Provide technical guidance on data feasibility for proposed AI initiatives Document data integration patterns, best practices, and lessons learned for knowledge sharing Support executive briefings by providing data-driven insights on AI readiness and capability gaps