Data Platform Architect

• 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 integration reliability and performance issues affecting production jobs data products and operational analytics.
• Design data models supporting machine learning analytics and business intelligence requirements including integrations with Tableau Power BI and Qlik Sense.
• Build and support integrations with MAVEN Smart Systems/Palantir Foundry environments and other enterprise systems.
• Collaborate with engineers data scientists BI analysts product managers platform and security teams and mission stakeholders to align architecture decisions with delivery priorities.
• Participate in design sessions technical reviews sprint activities demonstrations and cross-team problem solving.
• Maintain technical documentation supporting implementation consistency reuse operational handoff and long-term supportability.

• 5 years of technical experience including 3 years designing or implementing production solutions on Databricks Palantir Foundry or similar modern data platforms.
• Strong experience with Python SQL PySpark and Spark SQL for scalable data-processing workflows.
• Experience with Palantir Foundry or comparable enterprise analytics platforms including pipeline development governed data delivery lineage and operational analytics.
• Experience designing and operationalizing data pipelines transformation workflows and data products supporting structured and unstructured data.
• Hands-on knowledge of Databricks platform capabilities such as Delta Lake Workflows MLflow Unity Catalog or similar platform-native services.
• Familiarity with DataOps DevOps and MLOps practices including CI/CD version control testing deployment monitoring and operational support.
• Strong understanding of data quality metadata management lineage access control and governance within secure or regulated environments.
• Experience troubleshooting architecture integration performance and operational issues across distributed data platforms.
• Ability to establish technical standards guide architecture and implementation decisions and clearly communicate technical concepts to technical and non-technical stakeholders.

• Deep Databricks expertise including medallion architecture Delta optimization workload tuning cluster and job strategy and production ML enablement.
• Experience implementing solutions in Palantir Foundry including data-pipeline organization governed data assets operational workflows and integrations.
• Experience with Git-based CI/CD pipelines infrastructure and deployment tooling and cloud-native platform services.
• Experience supporting the ML lifecycle including model packaging registration deployment monitoring and inference-workflow integration.
• Knowledge of enterprise data integration API-based data exchange and secure cross-platform interoperability.
• Experience with Advana/MAVEN Smart System (Palantir Foundry) or similar DoD enterprise analytics environments.
• Prior experience supporting Department of Defense Intelligence Community or other Federal mission environments.

Benefits: