• Diseñar y mantener pipelines de datos ETL/ELT escalables utilizando Python. • Integrar datos de múltiples fuentes y formatos (JSON, CSV, XML). • Gestionar flujos de trabajo con DBT y herramientas de orquestación como Airflow, Prefect o Dagster. • Optimizar y extender el framework de datos Elastic Hierarchy. • Garantizar estándares de gobernanza, calidad y seguridad de los datos.

• Design, develop, and maintain scalable data pipelines to ingest, transform, and curate logistics, maintenance, and supply chain data from multiple USMC systems. • Implement robust ETL/ELT workflows to support analytics, reporting, and data warehousing initiatives. • Collaborate with data analysts, software engineers, and system integrators to ensure high-quality data delivery for operational and strategic use cases. • Optimize database performance, manage data models, and ensure data accessibility and consistency across environments. • Work with NIWC Atlantic and USMC stakeholders to translate logistics data requirements into technical solutions. • Integrate data from authoritative USMC logistics systems such as GCSSMC, TLS, and other readiness and sustainment datasets. • Ensure compliance with DoD cybersecurity, RMF, and data governance standards when designing and deploying solutions. • Support the development of dashboards, APIs, and analytics tools that improve supply chain visibility and asset readiness. • Troubleshoot data issues, perform root cause analysis, and implement corrective actions to maintain data integrity.

• Build scalable, high-performance data infrastructure that transforms complex transactional and financial data into trusted datasets for regulatory reporting and business operations. • Develop reliable data pipelines and data products that power regulatory reporting, financial operations, analytics, and operational insights while ensuring data accuracy and reliability. • Design, build, and optimize modern data models and distributed data pipelines. • Leverage AI throughout the software development lifecycle to improve engineering productivity, strengthen data quality, and build trusted data products. • Collaborate with cross-functional stakeholders to translate complex business and regulatory requirements into scalable, analytics-ready data models and solutions. • Mentor Data Engineering teammates by sharing best practices, providing technical guidance, and helping raise engineering standards across the team. • Collaborate in an agile environment to deliver scalable data solutions that support evolving business and regulatory needs.

• Build, transform, and process datasets to keep data accurate, reliable, and available for analytics and business decisions. • Maintain high-quality data infrastructure supporting reporting, analytics, and operational insights, including pipeline ingestion, transformation and modeling, and Level 1 support for pipeline failures, monitoring, and data quality.