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Job Description
About the Team & Role
Auger is building an autonomous operating system for the supply chain. Our customers rely on Auger to understand reality and change it: reporting, AI-powered decision support, and write-back execution systems that operate at scale.
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. You are expected to own data correctness, semantic correctness, and operability for the data lifecycle.
What You’ll Do
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.
What You Bring
- 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.