Data Engineer

CALIBRE is an employee-owned mission focused solutions and digital transformation company. CALIBRE is seeking an experienced Data Engineer to join a modernization team driving ETL and data modernization from legacy applications into AWS native services for Army systems. This role converts mission and business requirements into governed data pipelines, analytical models, semantic data structures, dashboards, and workflow applications that enable timely, trusted decisions.

This is a hands-on delivery role. The successful candidate will partner with mission owners, data stewards, engineers, analysts, and platform teams to integrate authoritative data, improve data quality, build reusable analytics, and transition solutions into sustained operations.

• Partner with functional stakeholders to define decision problems, success measures, data requirements, and minimum viable analytic products.
• Ingest, profile, clean, transform, and integrate structured and unstructured data from authorized enterprise and legacy sources.
• Build and maintain scalable data pipelines, curated datasets, and reusable analytical data products using Python, SQL, PySpark/Spark, and platform-native capabilities.
• Develop and maintain data models, ontology-aligned objects, transformations, workflows, dashboards, and user-facing applications.
• Design mission-appropriate analytical methods, including forecasting, anomaly detection, optimization, or classification when supported by data quality and operational need.
• Develop clear visualizations, dashboards, and executive-ready readouts using Vantage/Foundry tools and as required, Power BI, Tableau, Qlik, or comparable approved tools.
• Implement data-quality checks, lineage documentation, validation tests, model-performance monitoring, and reproducible analytic workflows.
• Apply platform, data-governance, cybersecurity, access-control, and release requirements; coordinate with data owners and stewards to ensure proper use of authoritative data.
• Use Agile delivery practices: refine requirements, estimate work, demonstrate increments, document solutions, and manage technical debt.
• Train end users and analysts; create concise technical documentation, data dictionaries, user guides, and sustainment handoffs.
• Communicate findings, limitations, assumptions, and recommended actions clearly to both technical teams and senior nontechnical stakeholders.
• Deliver [2–4] production-ready data products supporting validated mission decisions.
• Reduce manual data preparation or reporting time by 50% for targeted workflows.
• Establish documented data lineage, data-quality rules, and sustainment procedures for all delivered products.
• Achieve stakeholder acceptance and measurable adoption of dashboards, workflows, or analytic applications.
• Transition of reusable code, documentation, and operating procedures to the designated sustainment team.