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