Data Analytics Platform Engineer Jobs (NOW HIRING)

• 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.

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