The Principal Software Development Engineer will lead the data engineering team, develop data systems, ensure quality controls, and mentor engineers. Responsibilities include managing data lifecycle, implementing frameworks, and collaborating with cross-functional teams.
Build at Auger
What You’ll Do
What You Bring
Auger is the autonomous operating system for supply chains. It connects enterprise supply chain systems—ERP, WMS, TMS—into a single data layer, then uses AI to detect problems, evaluate trade-offs, and execute decisions automatically. The platform eliminates the coordination tax: the time and capital lost when disconnected systems force humans to become the integration layer between planning and execution. Actions that previously required days of meetings and manual coordination happen in seconds, within constraints the customer defines.
Founded by Dave Clark and backed by $100M from Oak HC/FT. Headquartered in Bellevue, Washington.
About the Team & RoleBuild the data foundation that powers Auger’s Supply Chain OS, AI systems, and execution workflows.
Auger is building an operating system for supply chain teams. 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 at a very high bar: you own substantial parts of the transformation layer that turns messy, customer-shared data into a unified, production-grade ontology powering analytics, AI workflows, and execution systems.
This is not a “move data from A to B” role. You are expected to own semantic correctness, operability, and durability for the systems you touch—while working within platform standards and influencing improvements across the team.
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. You will troubleshoot and resolve issues across the current data platform and codebase, including complex notebook-driven pipelines and SQL-heavy transformations.
- Own the evolution of the data lifecycle from ingestion through production-ready ontology, including standards for medallion-style lakehouse pipelines, schema contracts, and clear promotion paths from development to production.
- Practice test-driven engineering for data: define what "correct" means for critical datasets, encode that in tests and checks, and use failures to drive fixes and systemic improvements—not one-off patches.
- Shape technical direction for the data platform: boundaries between layers, evolution strategies, and correctness guarantees downstream systems can rely on.
- Drive pragmatic data governance engineers will adopt: producer/consumer contracts, schema evolution norms, ownership expectations.
- Represent data engineering in architecture and design discussions; make tradeoffs explicit among freshness, cost, complexity, and risk, and document decisions others can execute.
- Institutionalize observability and reliability engineering for data: SLIs/SLOs where appropriate, monitoring, incident response, post-incident learning, backfill/replay strategy, and elimination of recurring failure modes.
- Design and implement reusable, agentic frameworks across heterogeneous customer data sources so the team can rapidly discover schemas and semantics, draft transformation logic, generate and iterate on SQL, and run end-to-end troubleshooting workflows in a consistent, auditable way.
- Mentor engineers on AI-native discipline and operational rigor, and set expectations for quality bars on AI-assisted output.
- Partner across product, science, and data-tools platform to translate ambiguous needs into durable designs—aligning semantics, delivery, and operability with what customers experience in the product.
- Degree in Computer Science, Mathematics, Statistics, or another data-intensive discipline, with substantive principal-level engineering experience.
- 10+ years of professional development experience, including 8+ years hands-on across SQL and Python (strong familiarity with at least one large-scale batch engine such as Spark; Scala/Flink/Beam a plus).
- 8+ years in data management (structured and semi-structured), modern warehouses/lakehouses, and schema design in complex domains — with proven production ownership at scale: on-call, incidents, lakehouse architecture, ETL hardening, and long-term reliability improvements.
- Agent-native fluency: AI-assisted development for data work with rigorous validation — you know when generated SQL or pipelines are wrong and how to prove they are right.
- Experience defining data quality, observability, anomaly detection, or reliability standards and getting teams to adopt them.
- Technical leadership: lead through ambiguity, set direction for frameworks and conventions, mentor others, and communicate clearly with technical and non-technical partners — including crisp written technical strategy when stakes are high.
- A plus if you have prior experience in supply chain, planning, or fulfillment domains.
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.
Top Skills
Beam
Flink
Python
Scala
Spark
SQL
Auger Bellevue, Washington, USA Office
Bellevue, WA, United States, 98004
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