Software Development Engineer - AI Enablement

AI Enablement Engineering Team Role

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.

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/lake houses, 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 promo table.
• 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.