Data Engineer

Build and operate the streaming and sensor-data foundation that powers machine learning by ingesting, cleaning, standardizing, and delivering trusted datasets.

Responsibilities

• Assess available interfaces, message formats, historical coverage, data volumes, arrival rates, and known data quality limitations.
• Record investigation results to support implementation planning and capacity decisions.
• Create maintainable workflows for both streaming and historical ingestion across databases, APIs, files, and application-specific interfaces.
• Implement configurable filtering so downstream systems receive the required messages and measurements.
• Compare processing, storage, and access options using representative workloads with attention to reliability, interoperability, operational complexity, and cost.
• Document architecture and decision points to support evolution as requirements become clearer.
• Define consistent schemas covering timestamps, units, identifiers, and source metadata while preserving measurement meaning.
• Add validation and traceability, and establish dependable access methods in partnership with the ML team.
• Enable future feature work, labeling, and training-data workflows.
• Build and maintain tested, documented pipelines with repeatable deployment, monitoring, and appropriate access controls.
• Address duplicate, missing, delayed, and out-of-order data by implementing recovery and reprocessing procedures.

Requirements

• Experience designing, implementing, and operating production streaming and batch data pipelines and their supporting storage systems.
• Strong software engineering practices: maintainable code, automated testing, version control, and deployment.
• Practical understanding of streaming behavior, including filtering, consumer progress, buffering, failure recovery, and replay.
• Experience working with time-series, telemetry, sensor, or similarly complex data, including schema evolution, partitioning, and efficient access patterns.
• Ability to investigate unfamiliar systems, weigh technical tradeoffs, and communicate recommendations while balancing reliability, performance, maintainability, and cost.

Technologies

• Python
• AWS

Valuable Experience

• Python-based data processing and AWS or comparable cloud infrastructure.
• Radar, OPIR, or other sensor measurement data.
• Workflow orchestration, containers, infrastructure as code, and automated delivery.
• Dataset versioning, feature generation, labeling, or training-data preparation.

Location: Colorado Springs, CO; Denver, CO (onsite)

Salary: USD 120,000 - 150,000 per yearly