Principal Software Development Engineer
Build at Auger
Auger is the autonomous operating system for supply chains - the layer that finally allows disparate systems like ERP, WMS, and TMS to work together instead of against each other.
Most supply chain software surfaces problems and waits for a human to act. Auger solves them. Our AI detects disruptions, evaluates trade-offs, and executes decisions automatically - moving from signal to action in seconds, not weeks. We eliminate the Coordination Tax: the billions in capital and time lost when disconnected systems force the best people in the business to become the Human API between planning and execution.
At Auger, we design autonomy into our systems. We expect the same from our people.
That means:
- Clear ownership, not decision by consensus
- First principles over inherited patterns
- Shipping systems, not slide decks
- Fast feedback from reality, not opinions
If you want to build, ship, and iterate against reality, Auger is for you.
Auger was founded by Dave Clark and is backed by $150M from Oak HC/FT and Eclipse Capital. Our team works from Bellevue, WA and Dallas, TX.
About the Team
Every autonomous execution decision Auger makes starts with the AI Enablement Engineering team. This team transforms complex, disconnected customer data into a rich semantic backbone that gives our AI systems a deep understanding of each customer’s business. This team's work gives our AI the context it needs to reason, plan, and execute. This isn’t just a data engineering team. It's the team building the tools and the layer that gives Auger a competitive edge and enables delivery of measurable business outcomes for our customers.
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, writeperformant 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.
Compensation & Benefits
As part of our commitment to People Powered Greatness, we invest in our team members with competitive compensation and a comprehensive benefits to support your health, financial future, and daily life. The package includes medical, dental, and vision coverage, a 401(k) with company match, and commuter benefits. Total compensation may include a combination of a competitive base salary and equity. Your initial placement within our salary range will be based on your experience, qualifications.
The base pay range for this role is $280,000 - $330,000 per year.
Auger considers all qualified applicants for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. Additionally, our privacy policy is available at https://auger.com/privacy-notice/.