AI-Driven Data Engineering Lead

auger; Bellevue (KY); On-site; USD 280,000 - 330,000; Full time

Benefits offered by this job

Medical, dental, vision

401(k) with company match

Commuter benefits

Job summary

Auger is seeking a Principal Software Development Engineer to lead hands-on data engineering and AI framework development. You will shape medallion-style pipelines, guarantee data quality, and align data models with customer-facing product experiences.

You will partner with product, science, and data-tools teams to translate ambiguous needs into durable data designs, while driving reliability and observability across data and ML pipelines.

Qualifications

• 10+ years in professional software/data engineering development.
• 8+ years hands-on SQL and Python; strong with at least one large-scale engine (e.g. Spark).
• Experience designing and building large-scale data pipelines and lakehouse architectures.

Responsibilities

• Design and implement reusable, agentic AI frameworks across heterogeneous data sources and medallion-style pipelines.
• Own data engineering architecture, establish standards for lakehouse pipelines, data quality, and schema contracts.
• Collaborate with product, science, and data-tools teams to translate needs into durable data designs and contracts.
• Establish operating excellence: define correctness, testing, observability, SLOs, and incident response for data systems.
• Bridge data pipelines and ML pipelines, producing schema-bound datasets and reliable ML outputs.

Skills

SQL

Python

Spark

Data engineering

ETL

Data quality

Observability

Leadership

Communication

Education

Computer Science degree or equivalent

Tools

Databricks

Job description

Auger is seeking a Principal Software Development Engineer to lead hands-on data engineering and AI framework development. You will shape medallion-style pipelines, guarantee data quality, and align data models with customer-facing product experiences.

You will partner with product, science, and data-tools teams to translate ambiguous needs into durable data designs, while driving reliability and observability across data and ML pipelines.