- Clear ownership, not decision by consensus
- First principles over inherited patterns
- Shipping systems, not slide decks
- Fast feedback from reality, not opinions
- 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.
Skills Required
- Degree in Computer Science, Mathematics, Statistics, or equivalent experience
- 4+ years of professional development experience in SQL and Python
- 3+ years in data work involving modern warehouses/lakehouses
- Notebook fluency and ability to structure notebook work for review
- Experience in supply chain, planning, or fulfillment domains (preferred)
What We Do
About Auger Auger is a pioneering venture to build the world's first true end-to-end supply chain operating system. Founded and led by Dave Clark, former CEO of the Amazon Consumer Business and backed by an initial $100M from Oak HC/FT, Auger is building a future where global supply chains operate with the simplicity of today’s most intuitive consumer technologies. Revolutionizing global supply chains with an AI-powered OS unifying data for seamless, real-time insights, and powerful automation. Our Solution Auger is creating a new solution for companies seeking better options. Auger’s core strength lies in its deep AI-powered automation, paired with a consumer-grade user experience. This combination allows operators to handle complex tasks through simple, familiar tools. Need real-time inventory insights for next week’s shipment? Just ask. Actionable data appears instantly, enabling swift decisions—no complex queries or training required. Why We’re Different Traditional supply chain management is fragmented, relying on incompatible systems and inefficient workarounds. Many companies are stuck with “Franken-software”—patched-together solutions that fail to communicate effectively. Auger is different. We integrate deeply with existing systems, use AI to automate routine processes, and deliver a cohesive user experience that feels intuitive and natural, letting your team focus on what matters: driving growth, innovation, and sustainability. A Human-Centered Approach Broken supply chains don’t just impact businesses—they affect people. Delays mean products don’t reach shelves, miscommunications lead to overtime and burnout, and inefficiencies drive up costs and contribute to a growing carbon footprint. We believe supply chain problems are human problems, and we’re here to solve them. At Auger, we’re on a mission to make global supply chains more efficient, more sustainable, and ultimately, better for everyone.
