Job Purpose/Summary
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.
Duties and Responsibilities
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Own and deliver end-to-end data engineering capabilities supporting applied research, experimentation, geospatial analytics, backend services, and emerging data infrastructure.
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Build and maintain data pipelines that ingest, validate, transform, and organize structured, semi-structured, sensor, telemetry, and geospatial data.
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Develop backend services, APIs, and data interfaces supporting analytics, machine learning, and research workflows.
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Design and implement data models supporting analytical, operational, and geospatial use cases.
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Collaborate with researchers, data scientists, software engineers, and stakeholders to define requirements, estimate work, and deliver medium-scope technical capabilities.
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Contribute to data architecture and implementation decisions, balancing research velocity, data quality, scalability, maintainability, and technical complexity.
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Write clean, maintainable, and tested software for data processing, backend services, and analytical workflows.
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Debug data quality issues, pipeline failures, backend services, and integrations across databases, APIs, object storage, and analytical systems.
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Participate in code reviews, design discussions, planning, estimation, and technical documentation.
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Support onboarding and informal mentorship of junior engineers, interns, and new team members.
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Contribute to containerization, CI/CD, deployment, and operational support for research and development environments.
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Stay current with technologies and practices related to data engineering, backend software development, cloud-native systems, and geospatial analytics.
Qualifications
Required:
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Bachelor’s degree in Computer Science, Engineering, Data Engineering, Information Systems, or a related STEM field; or equivalent practical experience.
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3+ years of experience in software engineering, data engineering, backend engineering, or data-intensive application development.
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Ability to obtain and maintain a U.S. security clearance; U.S. Citizenship required.
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Strong proficiency in Python for data engineering, backend services, and data processing workflows.
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Experience designing, building, or maintaining data pipelines, backend services, APIs, or data-intensive software systems supporting analytical, geospatial, or machine learning workflows.
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Experience with relational databases, including PostgreSQL or similar systems, data modeling, query design, and structured data access patterns.
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Experience building standards-based APIs, including REST, using modern Python web frameworks, such as FastAPI or similar, including data validation and asynchronous request handling.
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Familiarity with Python data processing libraries, such as Pandas, Polars, or PyArrow, for transforming and working with analytical or application data.
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Familiarity with geospatial data concepts, spatial data formats, or demonstrated ability to work with location-based datasets.
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Experience deploying applications or services in cloud environments, such as AWS, and working with containerized systems such as Docker.
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Experience with CI/CD pipelines and version control systems, such as GitHub or GitLab, including automated testing and code review workflows.
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Experience implementing testing strategies, including unit, integration, and data validation tests.
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Familiarity with observability and debugging practices, including logging, monitoring, and troubleshooting distributed systems.
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Working knowledge of security best practices, including authentication, authorization, and secure data handling.
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Experience with at least one systems-oriented language, such as Go or Java, beyond Python.
Preferred:
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Experience with geospatial data handling, spatial databases, spatial data formats, or geospatial analytics workflows using tools such asPostGIS, Apache Sedona, GeoPandas, Shapely, GDAL, Rasterio, Cloud Optimized GeoTIFF, GeoParquet, or related technologies.
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Familiarity with lakehouse, streaming, or analytical data architectures using technologies such as Apache Iceberg, Delta Lake, Parquet, Arrow, object storage, Trino, DuckDB, Spark, Kafka, or similar tools.
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Experience developing data infrastructure or analytical frameworks that support machine learning, feature generation, experimentation, model evaluation, or applied research workflows.
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Experience working with sensor-based, telemetry, time-series, mobility, geospatial, or other high-volume real-world data systems.
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Experience supporting applied research, rapid prototyping, or experimental data workflows.
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Experience with edge, real-time, streaming, event-driven, or latency-sensitive applications.
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Familiarity with infrastructure-as-code tools and practices, such as Terraform.
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Familiarity with real-time communication and service integration patterns, such as WebSockets, gRPC, event-driven interfaces, or message-based architectures.
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Experience supporting mission-critical, customer-facing, fielded, or operationally relevant systems.
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Experience with modern frontend development, such as TypeScript or React, for internal tools, dashboards, or lightweight operational interfaces.
Working conditions
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Employees may be called upon to participate in in-person meetings, trainings, or company functions at Knowmadics offices or other designated locations. Travel in support of business operations may also be required, and employees are expected to comply with these obligations as part of their position.
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Candidate should live within driving distance of the following areas: Wichita, KS; Lawton,OK; or Round Rock, TX
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Some weekend work may be required based on project deadlines or operational needs.
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Estimated Travel:5-15%
Physical requirements
May include sitting or standing for extended periods, working with computers and technical equipment, and occasionally lifting or moving materials or tools.
Direct reports
None
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.
