• Supports ECS/EAD (Enterprise Case Selection/Enterprise Anomaly Detection) data engineering tasks including ingestion scripting, basic transformation logic, and data preparation activities for analytics and operational workloads. • Assists senior engineers in building and maintaining pipelines, validating data accuracy, and troubleshooting low‑complexity ingestion errors. • Performs initial schema mapping, data profiling, and documentation updates. • Works with analysts and data scientists to ensure datasets are complete, accessible, and aligned with ECS/EAD mission and compliance requirements

• Supports ECS/EAD (Enterprise Case Selection/Enterprise Anomaly Detection) data engineering efforts with a focus on automation and repeatable data processing workflows. • Builds automated data validation routines, ETL test harnesses, and monitoring scripts to ensure pipeline reliability, data integrity, and compliance with agency standards. • Implements ingestion and transformation components, integrates with cloud data services, and resolves pipeline defects through automated checks. • Collaborates with senior engineers, analysts, and cloud teams to ensure data flows are accurate, secure, and aligned with ECS/EAD modernization patterns.

• Transform, model, and integrate raw data of varying quality from a wide range of data sources into usable, documented and high quality data and intelligence products by applying data modeling and statistical techniques. • Identify, research, and develop new statistical approaches and new types of data to improve and extend our core healthcare data products. • Lead feature and product development cycles from defining Customer problem statements through to delivering solutions. • Work directly with product managers and cross-functional stakeholders to influence and build our Product development plans and roadmap. • Provide thought leadership on data science techniques and mentoring to junior data engineers. • Document and communicate technical and quantitative concepts, schemas, and data product usage guidelines with appropriate levels of detail for internal and external stakeholders.

• Own the end-to-end data architecture for the Data Warehouse Foundation, designing for AI-first consumption across GPT assistants, AI agents, predictive models, and operational intelligence — in addition to BI and reporting. • Lead data modeling across all 11 departments, designing canonical enterprise data models that serve cross-functional AI and analytics use cases without duplication or fragmentation. • Design and implement the contextual intelligence layer — including RAG architecture, vector store strategy, knowledge base ingestion pipelines, and document and unstructured data processing — that powers Agiloft's enterprise knowledge system. • Build and maintain the agentic data integration layer: real-time and near-real-time data access patterns, agent memory and state persistence design, orchestration data requirements, and agent output integration back into the warehouse. • Own the AI/ML feature layer — feature engineering strategy and standards, training data pipeline design, feature store architecture, and model output integration — enabling predictive analytics across churn, pipeline health, and operational forecasting. • Design and govern the operational data and GPT context layer, including structured context feed design for GPT assistants, data freshness and access SLAs for AI use cases, and cross-departmental data reuse standards. • Lead the Data Warehouse Foundation build in partnership with the external consulting team — setting architecture standards, reviewing implementation against AI-first principles, and ensuring the five-wave build plan delivers a foundation that serves the full intelligence architecture. • Design and manage data ingestion, ELT/ETL, and orchestration pipelines across all source systems, ensuring reliability, performance, and cost efficiency. • Establish and enforce AI data engineering standards across the organization — prompt-adjacent data design, agent data access patterns, reusable pipeline components, and quality assurance processes. • Own data access policy design and least-privilege access controls in partnership with Security, ensuring data made available to AI systems is governed, auditable, and compliant. • Define data quality standards and monitoring processes for AI-consumed data, where quality failures have direct impact on model and agent performance. • Partner with the Principal Data and Integrations Architect on infrastructure design, ensuring data modeling and AI consumption requirements are incorporated into pipeline and architecture decisions from the start — not retrofitted after build. • Partner with the VP FP&A and Manager of BI & Data to ensure the semantic and metrics layers are technically sound and serve both AI use cases and reporting requirements. • Manage the AI Ops data architecture roadmap, translating business and AI use case requirements from all 11 departments into sequenced, prioritized technical work. • Maintain documentation and knowledge transfer standards for all data architecture, pipelines, and integration patterns — ensuring AI Ops-built infrastructure is reusable, auditable, and not dependent on any single individual. • Collaborate with the AI Agent Engineer and GPT & AI Systems Lead to ensure data infrastructure supports agent orchestration, retrieval-augmented generation, and multi-step reasoning workflows. • Define the roadmap for data science and AI data work in partnership with the VP of AI Operations — this role does not take direction from IT on resource allocation or prioritization. All roadmapping is managed within AI Operations. • Evaluate and recommend data tooling, frameworks, and platform components in alignment with AI Ops' technology-agnostic, build-for-leverage approach. • Other duties as assigned.