Software Development Engineer - AI Enablement
Auger
About the Role
This role involves owning a significant portion of the data transformation layer, converting raw customer data into a unified, production-grade ontology that powers analytics, AI workflows, and execution systems. Responsibilities include managing the data lifecycle, implementing test-driven habits for data quality, and contributing to reusable patterns and tooling.
Requirements
Candidates need a degree in a data-intensive discipline or equivalent experience, along with 4+ years of professional development experience featuring strong hands-on SQL and Python in production environments, preferably with large-scale batch processing like Spark. A minimum of 3 years in data work, modern lakehouse/warehouse patterns, and practical schema design is also required.
Full Job Description
About the Team & Role
What You’ll Do
- Own your slice of the data lifecycle (ingestion → curated layers → production-ready outputs), including medallion-style patterns and schema contracts, within guidance from senior engineers and platform conventions.
- 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.
- Work in an agent-native style: use AI coding agents to move faster on investigation, SQL iteration, and refactors — paired with review, reproducible steps, and proof (tests, queries, invariants) before production.
- Contribute to reusable patterns and tooling so the team can discover schemas, draft transforms, generate SQL faster, and troubleshoot with less one-off work.
- Operate what you build: monitoring and alerting as appropriate, participating in incidents for your areas, executing backfills, and following through so issues do not repeat.
- Reduce local complexity: simplify where you can, remove redundancy, and prefer shared approaches when they clearly reduce risk.
- Partner with product, science, and platform teammates to clarify requirements, flag tradeoffs early, and deliver work that holds up beyond the first customer.
What You Bring
- 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 — with hands-on experience in 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.
- Agent-native fluency with verification: you treat generated SQL/pipelines as proposals until proven.
- Ownership mindset: you debug methodically, drive work to completion, connect your work to customer outcomes, and leave the codebase better than you found it.
- Clear communication and collaboration; you can drive a feature or multi-step project in your domain, seek input on design, and incorporate feedback.
- A plus if you have experience in supply chain, planning, or fulfillment domains.
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Compensation
AI Est. Total Comp
$230,000
Details
Location
Bellevue
Work Type
On-site
Seniority
mid level
Experience
2-5 years
Category
ml ai
Quality Score
6.4