Job description

About the Role Build the data foundation that powers Auger’s Supply Chain OS, AI systems, and execution workflows. Auger is building an operating system for supply chain teams. Our customers rely on Auger to understand reality and change it: reporting, AI-powered decision support, and write-back execution systems that operate at scale. At the core of this system is Data Engineering. This role sets direction for and owns the evolution of the transformation of messy, customer-shared data into a unified, production-grade ontology that directly powers analytics, AI workflows, and execution systems. This is not a “move data from A to B” role. This is system-level semantic ownership at the heart of the product. What You’ll Do As a Senior Data Engineer, you will leverage new and existing customer data sources ingested into Auger’s core data lake and transform them into our ontology. We’re seeking teammates who love data of all kinds, are masters of building efficient, scalable, operable and durable data systems, and are ready to take hands-on ownership beyond individual pipelines in the following areas: • Own and evolve the data lifecycle across systems, from ingestion through production-ready ontology • Ingest data from databases, data streams, batch files, and incremental feeds • Define standards for and operate medallion-style lakehouse pipelines (bronze → silver → gold) • Transform raw inputs into a consistent digital twin of supply chain reality that scales across customers • Serve high-quality data to analytics, AI workflows, and write-back systems with clear correctness guarantees • Own data correctness and reliability in production, including monitoring, on-call, incident response, and post-incident systemic improvements • Define and enforce data quality checks, validations, and robust backfill strategies • Use AI-assisted tools responsibly, setting expectations for review, validation, and production readiness of generated code • Reduce complexity at scale by simplifying pipelines, eliminating redundancy, and automating recurring workflows • Partner with product, science, and platform tooling teams to translate ambiguous needs into durable technical designs What You Bring • Degree in Computer Science, Mathematics, Statistics, or other data-intensive discipline with substantive engineering experience • 5+ years demonstrated development experience using technologies like Python, SQL, Scala, Spark, Flink, and Beam • 5+ years demonstrated experience in data management (structured and unstructured) and modern database technologies • Proven experience owning and evolving large-scale production data systems in distributed environments • Hands-on experience designing and operating lakehouse or warehouse architectures at scale • Strong schema design skills and deep intuition for data modeling in complex domains • A production mindset—you’ve owned critical systems, led incident resolution, and driven long-term fixes • Experience supporting AI/ML or AI-powered products where data quality directly impacts outcomes • Familiarity with streaming or incremental processing at scale • Experience defining data quality, observability, anomaly detection, or reliability standards • A deep curiosity and eagerness to problem solve in ambiguous, high-impact problem spaces without a playbook • Ability to lead through ambiguity with urgency, patience, and good judgment while raising the bar for others • Strong communication and collaboration skills • A plus if you have prior experience in the supply chain domain

Requirements

Must-haves

Build the data foundation that powers Auger’s Supply Chain OS, AI systems, and execution workflows.
This is not a “move data from A to B” role. This is system-level semantic ownership at the heart of the product.
What You’ll Do
Own and evolve the data lifecycle across systems, from ingestion through production-ready ontology
Ingest data from databases, data streams, batch files, and incremental feeds
Define standards for and operate medallion-style lakehouse pipelines (bronze → silver → gold)
Transform raw inputs into a consistent digital twin of supply chain reality that scales across customers
Serve high-quality data to analytics, AI workflows, and write-back systems with clear correctness guarantees
Own data correctness and reliability in production, including monitoring, on-call, incident response, and post-incident systemic improvements
Define and enforce data quality checks, validations, and robust backfill strategies
Use AI-assisted tools responsibly, setting expectations for review, validation, and production readiness of generated code
Reduce complexity at scale by simplifying pipelines, eliminating redundancy, and automating recurring workflows
Partner with product, science, and platform tooling teams to translate ambiguous needs into durable technical designs
Degree in Computer Science, Mathematics, Statistics, or other data-intensive discipline with substantive engineering experience
5+ years demonstrated development experience using technologies like Python, SQL, Scala, Spark, Flink, and Beam
5+ years demonstrated experience in data management (structured and unstructured) and modern database technologies
Proven experience owning and evolving large-scale production data systems in distributed environments
Hands-on experience designing and operating lakehouse or warehouse architectures at scale
Strong schema design skills and deep intuition for data modeling in complex domains
A production mindset—you’ve owned critical systems, led incident resolution, and driven long-term fixes
Experience supporting AI/ML or AI-powered products where data quality directly impacts outcomes
Familiarity with streaming or incremental processing at scale
Experience defining data quality, observability, anomaly detection, or reliability standards

Nice-to-haves

A plus if you have prior experience in the supply chain domain

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonsqlml

Platform: gem
Effort: medium