Data Engineer

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Full Time Professional Lawton, OK, US

The Data Engineer owns and delivers end-to-end data engineering features that support applied research, experimentation, and emerging analytical capabilities. This role develops data pipelines, backend services, data models, and supporting infrastructure for geospatial, sensor, telemetry, and analytical data systems.

The role is responsible for delivering data-oriented capabilities from design through validation, including building ingestion workflows, transforming and modeling datasets, developing APIs and service interfaces, and integrating with analytical storage systems. The Data Engineer contributes to system design, making informed tradeoffs across speed of research iteration, data quality, scalability, and implementation complexity.

The Data Engineer works closely with researchers, data scientists, software engineers, and product stakeholders to clarify requirements, define acceptance criteria, and translate experimental concepts into usable technical solutions. The role handles moderate ambiguity with support, breaks down medium-scope work into actionable tasks, and contributes to planning and estimation.

As a collaborative team member, this role produces clear technical documentation, participates in design discussions and code reviews, and helps coordinate dependencies across data, backend, and research workflows. The person in this role demonstrates solid working knowledge of the domain, anticipates common edge cases in real-world data, and incorporates appropriate safeguards through validation, testing, instrumentation, and monitoring.

In partnership with cross-functional teams, this role helps move promising research capabilities from prototype toward reusable data infrastructure, emphasizing rapid iteration, practical engineering judgment, and operational rigor to support reliable experimentation, evaluation, and future maturation.

• Own and deliver end-to-end data engineering capabilities supporting applied research, experimentation, geospatial analytics, backend services, and emerging data infrastructure.
• Build and maintain data pipelines that ingest, validate, transform, and organize structured, semi-structured, sensor, telemetry, and geospatial data.
• Develop backend services, APIs, and data interfaces supporting analytics, machine learning, and research workflows.
• Design and implement data models supporting analytical, operational, and geospatial use cases.
• Collaborate with researchers, data scientists, software engineers, and stakeholders to define requirements, estimate work, and deliver medium-scope technical capabilities.
• Contribute to data architecture and implementation decisions, balancing research velocity, data quality, scalability, maintainability, and technical complexity.
• Write clean, maintainable, and tested software for data processing, backend services, and analytical workflows.
• Debug data quality issues, pipeline failures, backend services, and integrations across databases, APIs, object storage, and analytical systems.
• Participate in code reviews, design discussions, planning, estimation, and technical documentation.
• Support onboarding and informal mentorship of junior engineers, interns, and new team members.
• Contribute to containerization, CI/CD, deployment, and operational support for research and development environments.
• Stay current with technologies and practices related to data engineering, backend software development, cloud-native systems, and geospatial analytics.

• Bachelor’s degree in Computer Science, Engineering, Data Engineering, Information Systems, or a related STEM field; or equivalent practical experience.
• 3+ years of experience in software engineering, data engineering, backend engineering, or data-intensive application development.
• Ability to obtain and maintain a U.S. security clearance; U.S. Citizenship required.
• Strong proficiency in Python for data engineering, backend services, and data processing workflows.
• Experience designing, building, or maintaining data pipelines, backend services, APIs, or data-intensive software systems supporting analytical, geospatial, or machine learning workflows.
• Experience with relational databases, including PostgreSQL or similar systems, data modeling, query design, and structured data access patterns.
• Experience building standards-based APIs, including REST, using modern Python web frameworks, such as FastAPI or similar, including data validation and asynchronous request handling.
• Familiarity with Python data processing libraries, such as Pandas, Polars, or PyArrow, for transforming and working with analytical or application data.
• Familiarity with geospatial data concepts, spatial data formats, or demonstrated ability to work with location-based datasets.
• Experience deploying…