Data Engineer

About the role

The Data Engineer is responsible for end-to-end data engineering capabilities that enable applied research, experimentation and geospatial analytics. Working with researchers, data scientists, software engineers and product stakeholders, the role builds data pipelines, backend services, APIs and data models, ensures data quality and scalability, and contributes to architecture, documentation and mentorship within a collaborative, cloud-native environment.

About the company

Knowmadics is a software company (51-200 employees) that provides real-time intelligence solutions for national-security operators. Its 360° Aware® Suite fuses AI-driven data from thousands of sensors across land, air, sea, cyber and space to give defense, intelligence, law‑enforcement and enterprise teams situational awareness and decision‑making advantage. Headquartered in Wichita, Kansas with additional innovation offices in Virginia, North Carolina, Texas and Oklahoma.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Engineering, Information Systems, or related STEM field, or equivalent practical experience.
• Minimum 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 such as PostgreSQL, including data modeling, query design, and structured data access patterns.
• Experience building standards-based REST APIs using modern Python web frameworks (e.g., FastAPI) with data validation and asynchronous handling.
• Familiarity with Python data processing libraries (e.g., Pandas, Polars, PyArrow).
• Familiarity with geospatial data concepts or ability to work with location-based datasets.
• Experience deploying applications or services in cloud environments (e.g., AWS) and using containerization (Docker).
• Experience with CI/CD pipelines and version control systems (GitHub or GitLab), including automated testing and code review workflows.
• Experience implementing testing strategies (unit, integration, data validation tests).
• Familiarity with observability and debugging practices (logging, monitoring, troubleshooting distributed systems).
• Working knowledge of security best practices (authentication, authorization, secure data handling).
• Experience with at least one systems-oriented language such as Go or Java.
• Experience with geospatial data handling, spatial databases, or tools such as PostGIS, Apache Sedona, GeoPandas, Shapely, GDAL, Rasterio, Cloud Optimized GeoTIFF, GeoParquet.
• Familiarity with lakehouse, streaming, or analytical data architectures using technologies like Apache Iceberg, Delta Lake, Parquet, Arrow, object storage, Trino, DuckDB, Spark, Kafka.
• Experience developing data infrastructure or analytical frameworks supporting machine learning, feature generation, experimentation, model evaluation, or applied research workflows.
• Experience working with sensor-based, telemetry, time-series, mobility, geospatial, or high-volume real-world data systems.
• Experience supporting applied research, rapid prototyping, or experimental data workflows.
• Experience with edge, real-time, streaming, event-driven, or latency-sensitive applications.
• Familiarity with infrastructure-as-code tools such as Terraform.
• Familiarity with real-time communication and service integration patterns (WebSockets, gRPC, event-driven interfaces, message-based architectures).
• Experience supporting mission-critical, customer-facing, fielded, or operationally relevant systems.
• Experience with modern frontend development (TypeScript or React) for internal tools, dashboards, or lightweight operational interfaces.