This role involves owning aspects of data lifecycle for Auger's Supply Chain operating system, including schema design and data processing using AI and SQL.
Build at Auger
What You’ll Do
What You Bring
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 & RoleEvery 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.
As a Software Development Engineer, you have a solid data engineering background. You will deliver end-to-end on assigned pipelines and transformation work, help troubleshoot production issues, and consistently improve quality through tests, checks, and sound operational habits.
- Build and maintain data pipelines lifecycle. Ship production-grade transformation logic and operational outputs, using schema contracts and measurable validation.
- Work in an agent-native style—use AI tools to move faster on data exploration, data transformation, data queries, data investigation, and refactors. Contribute to reusable patterns and tooling (including agent-assisted workflows) so the team can discover schemas, draft transforms, generate SQL faster, and troubleshoot with less one-off work.
- Build and maintain the integration points between data pipelines and ML pipelines. Implement schema-bound datasets that transform pipeline outputs into ML-ready inputs, and write ML results back to the semantic layer following established contracts. Contribute to schema design and enforce data contracts that keep model logic cleanly separated from the system of record.
- Operate what you build: monitoring and alerting as appropriate, participating in incidents remediations, and following through so issues do not repeat. Practice test-driven habits for data: clarify correctness for the datasets you touch; add automated checks and regression coverage where it matters; turn bugs and incidents into fixes that stick.
- Partner with product, science, and platform teammates to clarify requirements, flag tradeoffs early, and deliver work that holds up to customers.
- Degree in Computer Science, Mathematics, Statistics, or another data-intensive discipline (or equivalent practical experience). 4+ years of professional development experience with strong hands-on SQL and Python in production (Spark or equivalent large-scale batch processing preferred; Scala/Flink/Beam a plus). 3+ years in data work (structured and semi-structured), modern warehouses/lakehouses, and practical schema design in evolving domains.
- Ownership mindset on production systems: you debug methodically, improve reliability over time, and connect your work to customer/product outcomes. Hands-on experience with lakehouse/warehouse patterns, incremental processing, and basic performance/cost awareness. Notebook fluency and the judgment to structure notebook work so it is reviewable and promotable.
- Validation-first habits for data: meaningful checks between layers, DQ where it counts, and regression protection for critical transforms. Agent-native fluency with verification—you treat generated SQL/pipelines as proposals until proven.
- Clear communication and collaboration: you ask good questions, drive work to completion, and leave the codebase better than you found it.
- A plus if you have 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 $150,000 – $200,000 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/.
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