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# Principal Software Development Engineer

- **Company:** AUGER INC.
- **Location:** Bellevue, WA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Acceptance Test-Driven Development, Information Engineering, Extract Transform Load (ETL), Data Systems, Distributed Systems, Python (Programming Language), Machine Learning, Operational Databases, Reliability Engineering, Software Engineering, SQL Databases, Apache Spark, Information Technology, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** October 26, 2026
- **Apply:** https://www.careerbuilder.com/job-details/principal-software-development-engineer-bellevue-wa--c4a7b2d1-a793-49c0-addd-c33576168a1c

## About the Role

Software Development, Software Engineering, Operating Systems, ERP (Enterprise Resource Planning), Supply Chain Management Software, Application Programming Interface (API), Artificial Intelligence (AI), Team Building, Customer/Client Research, Software Development, * Degree in Computer Science or another data-intensive field, with principal-level experience. 10+ years in professional development, including 8+ years hands-on with SQL and Python and strong familiarity with at least one large-scale engine (e.g. Spark). 8+ years across data management (structured and semi-structured), modern warehouses/lakehouses, ETL/validation, and schema design in complex domains.

 * Production ownership:Track record owning large-scale production data systems in distributed environments-on-call, incidents, and lasting reliability improvements (not just one-off fixes).
 * Engineering discipline for data: Test-driven habits for transforms-unit/integration patterns, contract tests between layers, and data quality checks tied to business meaning. Experience defining standards for quality, observability, anomaly detection, or reliability and getting teams to adopt them.
 * AI-native workflow: Comfortable with AI-assisted development for data work, with rigorous validation-you recognize when generated SQL or pipelines are wrong and know how to prove they're right.
 * Leadership & Communication: Technical leadership through ambiguity-set direction for frameworks and conventions, mentor others, communicate clearly both with customers and with internal technical and non-technical partners.
 * Deep curiosity in ambiguous, high-impact problems; sound judgment under urgency; patience to fix root causes, not symptoms.
 * A plus if you have prior experience in supply chain, planning, or fulfillment domains.

## Description

As a Principal Software Development Engineer, you bring a strong data engineering background. You will lead hands-on execution while raising the bar for how we build, validate, and operate data systems.

 * Design and implement reusable, agentic AI frameworks across heterogeneous customer data sources so the team can rapidly discover schemas and semantics, generate ETL transformation logic in medallion style that hydrates the gold semantic layer, write performant SQL, and run efficient end-to-end data troubleshooting in a consistent, scalable way. Mentor the team on AI-native best practices.
 * Own data engineering architectural designs and shape technical direction for the data team: standards for medallion-style lakehouse pipelines, boundaries between layers, evolution strategies, and data quality standards.
 * Partner across product, science, and the data-tools platform to translate ambiguous needs into durable designs-aligning data models, semantics, and schema contracts with what customers experience in the product. Align business validation rules and data contracts with the Science team, and requirement-definition contracts with the Product team.
 * Operating excellence: Practice operating excellence and test-driven engineering for data: define what "correct" means for critical datasets, and encode that in dev test prod paths. Institutionalize observability and reliability engineering for data: SLOs, monitoring, incident response, backfill/replay strategy, and elimination of recurring failure modes.
 * Own the interface where data pipelines and ML pipelines meet. Turn data pipeline outputs into schema-bound datasets that feed machine learning. Turn ML results into reliable writes to the semantic layer. Define and enforce clear schemas and contracts to decouple fast-moving model logic from the system of record.