- 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.
- 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.
Top Skills
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.
