Auger
Principal Software Development Engineer
Bellevue, USA · Principal
Sponsorship not specifiedDetected 119 days ago
PythonSQLMachine LearningSparkData EngineeringAgentic AIIncident ResponseSupply ChainLeadershipCommunication
About the role
- This role is data-centric software engineering.
- We hold a high bar for quality: you'll help turn messy, customer-shared data into a unified semantic layer that analytics, AI workflows, and execution paths can rely on.
- This is not a "move data from A to B" role.
Responsibilities
- 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.
- 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.
- Own the interface where data pipelines and ML pipelines meet.
- reporting, AI-powered decision support, and write-back execution systems that operate at scale.
Requirements
- 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).
- 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).
- Experience defining standards for quality, observability, anomaly detection, or reliability and getting teams to adopt them.
- What You Bring Degree in Computer Science or another data-intensive field, with principal-level experience.
Nice to have
- 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.
Benefits
- Turn data pipeline outputs into schema-bound datasets that feed machine learning.
Company info
- 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.
This listing is sourced directly from Auger's careers page and normalized into a canonical job model.