Data Engineer

CALIBRE Systems, Inc.•Arlington, VA

About The Position

CALIBRE is an employee-owned mission focused solutions and digital transformation company. CALIBRE is seeking an experienced Data Engineer to develop production-ready data products, analytics, and decision-support applications using Army Vantage and Department of War (DoW) War Data Platform (WDP) capabilities. The incumbent will translate mission and business questions 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 work 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.

Requirements

Experience as a Data Engineer
Python
SQL
PySpark/Spark
Vantage/Foundry data models
Power BI, Tableau, Qlik, or comparable approved tools
Agile delivery practices

Responsibilities

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 Vantage/Foundry 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.