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# Software Engineer - Manufacturing Data & Test Infrastructure

- **Company:** Astera Labs
- **Location:** San Jose, United States
- **Experience:** Expert
- **Salary:** $135,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Automation of Tests, Big Data, Computer Engineering, Continuous Integration, Data Systems, Software Debugging, Linux, Electronic Data Interchange (EDI), Firmware, Monitoring of Systems, Systems Analysis, Python (Programming Language), Linux System Administration, Operational Databases, Cloud Services, Runbook, Data Streaming, Systems Integration, Technical Data Management Systems, Test Data, Hardware Testing, Backend, Git Flow, Infrastructure Automation Frameworks, Information Technology, Deployment Automation, Data Pipelines
- **Published:** October 30, 2026
- **Apply:** https://startup.jobs/sr-software-engineer-manufacturing-data-test-infrastructure-astera-labs-10616087

## About the Role

* Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a rated field
 * 3+ years building and operating backend systems, data systems, developer infrastructure, or internal platforms
 * Strong Python skills, including production services, scripts, libraries, or automation tools
 * Experience with large-scale data processing and structured data pipelines
 * Practical understanding of changing data formats, data quality challenges, and production data collection
 * Experience with relational, analytical, or streaming data systems
 * Experience supporting CI/CD systems, Git-based workflows, Linux environments, and containerized or cloud-based services
 * Experience with monitoring and observability tools, and the ability to balance operations with longer-term engineering improvements, * MS or PhD in Electrical Engineering or Computer Engineering
 * Experience in semiconductors, hardware test, manufacturing test, or a similar engineering environment
 * Familiarity with production test data, yield analysis, or manufacturing diagnostics
 * Experience with public cloud services and secure external data exchange workflows
 * Experience with infrastructure automation and configuration management tools
 * Experience integrating collaboration and issue-tracking systems into automated workflows
 * Experience working with external vendors, labs, or manufacturing partners
 * Interest in AI-assisted failure analysis, debugging workflows, or internal knowledge tools
 * Strong interest in building durable internal platforms that scale with engineering and manufacturing needs

## Description

* Build, own, and improve data pipelines that collect, standardize, and organize manufacturing and test data.
 * Integrate data from multiple internal and external sources, including lab test systems and manufacturing partners.
 * Improve data quality, pipeline reliability, handling of changing formats, and visibility into pipeline health.
 * Add support for new products, data sources, and manufacturing partners with minimal manual effort.
 * Make the tools engineers use to access and analyze data faster and easier to use.

Test Automation and Failure Analysis

 * Maintain and improve automation systems that support hardware validation and production testing.
 * Keep build, test, and release workflows manageable, repeatable, and version controlled.
 * Operate and improve automated tools that process results, classify failures, and route issues to the right teams.
 * Reduce repetitive debugging through better classification, dashboards, and internal tooling.

Infrastructure, Reliability, and Monitoring

 * Operate services on Linux-based infrastructure using containers and cloud-hosted systems.
 * Support core platform services such as storage, scheduled jobs, and deployment automation.
 * Build and improve monitoring, alerting, and dashboards so issues are detected early.
 * Strengthen day-to-day operations with runbooks, alert tuning, and safer rollout practices.

Internal Tools and Cross-Functional Support

 * Support and extend shared telemetry and logging systems.
 * Build practical tools that help engineers explore logs, look up records, and diagnose failures faster.
 * Help build automated or AI-assisted debugging tools where they provide clear value.
 * Act as a technical point of contact for issues involving manufacturing data and test platforms.
 * Work closely with hardware, firmware, test, IT, and external partners to resolve system and data issues.

What Success Looks Like

 * New manufacturing and test data sources are added quickly and reliably.
 * Engineers can access trusted data without relying on specialized support.
 * Test automation and failure-analysis systems are stable, understandable, and maintainable.
 * Operational issues are detected early through effective dashboards and alerts.
 * Documentation and tooling are strong enough to support shared ownership across the team.