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# Data Engineer

- **Company:** Inspyr Solutions
- **Location:** Redmond, WA, United States (Remote available)
- **Salary:** $145,600.0 - $166,400.0
- **Contract:** Temporary contract
- **Skills:** Amazon Web Services, Big Data, Data Infrastructure, Data Transformation, Digital Architecture, Firmware, Python (Programming Language), Data Pipelines
- **Published:** August 1, 2026
- **Apply:** https://www.dice.com/job-detail/3f6709e3-b6e7-42a2-bddd-d8be5a5dea32

## About the Role

* Strong experience designing and building data pipelines
 + preferably in AWS
 * Advanced Python development skills
 + for data transformation, processing, and analysis
 * Demonstrated ability to build automated analytics
 + not just move the data - Anomaly detection, trend/degradation detection, regression detection across large datasets
 * Experience deploying reliable data infrastructure by creating functional dashboards or lightweight analysis tools.

NICE-TO-HAVE SKILLS

 * Experience in satellite, aerospace, telecommunications, or other hardware-focused engineering environments.
 * Comfortable leveraging AI-assisted development tools to improve development efficiency and productivity.

## Description

Join a cutting-edge PHY (Physical Layer) engineering team developing next-generation satellite broadband technology. This team is responsible for the radio link between satellites in orbit and ground terminals, as well as the telemetry systems that capture and analyze the health and performance of those communications.
As a Data Engineer, you''ll play a key role in building the cloud-based data infrastructure that transforms telemetry data into actionable insights. Your work will help engineers automatically identify performance trends, detect anomalies, and uncover firmware regressions across satellite contacts-improving the reliability and performance of a global satellite communications system.

THE OPPORTUNITY FOR YOU

 * Design, build, and own AWS-based data pipelines that ingest telemetry data from satellite-to-ground communications.
 * Develop scalable Python-based data transformation and processing workflows.
 * Build automated analytics that identify trends, anomalies, performance degradation, and firmware regressions across large telemetry datasets.
 * Create lightweight dashboards or analysis tools that make telemetry insights easily accessible to engineering teams.
 * Deploy reliable, production-ready data pipelines from exploratory prototypes.
 * Document architecture, workflows, and operational procedures to ensure long-term maintainability and successful knowledge transfer.


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