Data Platform Architect

Expression is seeking an experienced Data Platform Architect to provide architectural guidance, technical standards, and operational support for teams delivering secure, scalable data, analytics, and AI/ML solutions in mission environments.

The Data Platform Architect will work across engineering, data science, analytics, platform, and mission teams to guide implementation in Databricks and Palantir Foundry. This role will help delivery teams structure data pipelines, data products, analytics and ML workflows, and platform assets so solutions are consistent, reusable, governed, supportable, and production-ready.

The successful candidate will provide hands‑on guidance spanning data integration, Data Ops, Dev Ops, MLOps, governance, security, compliance, performance optimization, and platform operations while helping teams move solutions from prototypes into reliable production environments.

Clearance: Secret/Top Secret clearance required
Location: Falls Church, VA

• Provide hands‑on architectural guidance to teams implementing data pipelines, analytics workflows, data products, and AI/ML capabilities in Databricks and Palantir Foundry.
• Guide selection and implementation of platform‑native capabilities for data ingestion, transformation, orchestration, model execution, analytics, and data‑product delivery.
• Advise teams on appropriate use of Databricks, Foundry, and integrated cross‑platform architectures.
• Guide the transition of prototypes and notebook‑based solutions into reliable, maintainable production workflows.
• Establish and maintain technical standards for project structure, code organization, pipeline design, workflow orchestration, testing, metadata, lineage, documentation, and platform implementation.
• Develop reusable templates, reference architectures, and implementation patterns that improve consistency and accelerate delivery.
• Promote scalable approaches including medallion architecture, governed data publishing, reusable transformation logic, and shared analytics and ML components.
• Conduct technical reviews and provide actionable guidance to improve scalability, maintainability, reliability, and supportability.
• Guide CI/CD implementation for jobs, pipelines, notebooks, packaged code, models, and data products.
• Establish operational practices for deployment, environment promotion, monitoring, alerting, rollback, release management, observability, lineage, and data‑quality validation.
• Promote reproducible MLOps practices for model training, validation, packaging, registration, deployment, monitoring, batch inference, and lifecycle management using MLflow, Databricks workflows, and related capabilities.
• Design scalable ML inference approaches supporting production workloads and establish monitoring for model performance, data drift, and system health.
• Support self‑service ML capabilities that enable data scientists to efficiently deploy and monitor models.
• Define integration patterns for onboarding data sources, managing schema evolution, and connecting Databricks and Foundry with enterprise systems, applications, data warehouses, streaming platforms, APIs, and BI tools.
• Guide implementation of secure access controls, governed data sharing, metadata management, data catalogs, lineage, traceability, and audit‑ready workflows.
• Establish data‑quality standards and automated testing approaches for analytical and ML workloads.
• Partner with stakeholders to define data definitions, business logic, governance requirements, and compliant handling of structured and unstructured data.
• Advise teams on Spark optimization, workload design, workflow dependencies, storage and compute utilization, and other platform‑performance considerations.
• Identify and help resolve architecture,…