Data Engineer

Addx Corporation is hiring a Data Engineer to build a dependable data foundation for machine learning applications. This onsite role in Colorado Springs, CO and Denver, CO focuses on integrating and delivering cleaned, standardized datasets, with an emphasis on both streaming and historical ingestion. You will work with Python and AWS to operate reliable pipelines that the ML team can trust.

Salary: USD 120,000 - 150,000 per year.

What you’ll do

• Investigate available interfaces, message formats, historical coverage, data volumes, arrival rates, and data quality limitations.
• Document findings to inform implementation details and capacity planning.
• Build maintainable workflows for streaming and historical data across databases, APIs, files, and application-specific interfaces.
• Implement configurable filtering so downstream consumers receive the messages and measurements they need.
• Evaluate processing, storage, and access options using representative workloads, reliability requirements, interoperability, operational complexity, and cost.
• Document architectural decisions and support the evolution of the system as requirements become clearer.
• Define consistent schemas, timestamps, units, identifiers, and source metadata while preserving measurement meaning.
• Implement validation and traceability, and define dependable access methods with the ML team.
• Support future feature, labeling, and training-data workflows.
• Build tested and documented pipelines with repeatable deployment, monitoring, and appropriate access controls.
• Handle duplicate, missing, delayed, and out-of-order data, including recovery and reprocessing procedures.

What you bring

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

Tools and technologies

• Python
• AWS

Valuable additional 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.