Data Engineering

Scalable, reliable pipelines that move data between systems, handle transformations, and deliver clean data to downstream applications and analytics tools.

Engineering data environments specifically for AI consumption, including feature engineering, data labeling pipelines, and the high-throughput architectures needed to train and deploy large-scale models.

Structuring, normalizing, and managing health care data using industry-standard terminologies (e.g., ICD-10, CPT, LOINC, RxNorm, SNOMED) to enable interoperability and analysis across federal health systems.

Dashboards, reports, and analytics platforms that give program leaders real-time visibility into program performance, beneficiary outcomes, and operational efficiency.

Connecting disparate data sources, such as legacy databases, APIs, SaaS platforms, and cloud storage, into unified systems that enable agencies to work from a single source of truth.

Logical data modeling, metadata management, and documentation of data flows to ensure data is organized, findable, and usable across teams and systems.

Policies, standards, and practices that ensure data quality, security, and compliance — including data classification, access controls, and lineage tracking. We apply the rigorous standards outlined in the Office of Management and Budget (OMB) memorandum M-25-21, “Accelerating Federal Use of AI through Innovation, Governance, and Public Trust,” and agency-specific documentation, like the CMS AI Playbook, to ensure that data lineage, classification, and privacy controls meet the high bar required for federal AI and machine learning applications.

Specialized analysis, including geospatial mapping, statistical modeling, and pattern detection for agencies with complex analytical needs.