Data Engineer

Data Engineer

Mid-Level | Full-Time | On-Site | Reports to Director of FP&A

Position Summary

The Data Engineer is a mid-level individual contributor responsible for the systems and structures that make the company's data reliable, accessible, and useful. This role owns the design and operation of the ingestion pipelines, transformation logic, and data models that serve both business intelligence and AI initiatives.

This isn't a role that waits for fully formed requirements. You'll be expected to understand how the business operates and apply that knowledge to technical decisions. A pipeline that runs on time but models the business incorrectly hasn't done its job.

What You'll Do

Data Ingestion & Orchestration

• Design, build, and maintain ingestion pipelines from source systems into Snowflake
• Orchestrate workflows in Dagster so they're observable, reliable, and operationally sound
• Monitor pipeline health and data availability; investigate failures, fix root causes, and add alerting so issues surface early

Transformation & Modeling

• Develop and maintain dbt models that turn raw data into clean, tested, well-documented datasets
• Design dimensional models and data marts that support both reporting and machine learning
• Implement data quality checks and testing standards so downstream consumers can trust datasets without re-verifying them

AI Data Preparation

• Structure feature datasets and training data pipelines
• Partner with data science and AI teams on data quality, freshness, and structure requirements
• Contribute to the semantic layer that gives analytics and AI consumers consistent access to business metrics and entities
• Keep data lineage and governance in view

Business Partnership

• Build working knowledge of core business processes: product sales through dealership partners, claims administration, and cancellations
• Engage directly with business stakeholders to understand data needs and translate them into sound technical designs
• Apply business context to modeling decisions, pipeline priorities, and data quality standards

Platform Direction

• Advise on the strategic direction of the data engineering toolset, including platform updates and new capabilities
• Stay current on best practices in data engineering, AI data preparation, and orchestration

Adjacent Work

Dashboards and reporting belong to analysts and BI developers, but you may occasionally build or troubleshoot a report, or help an analyst work through a modeling issue at the source.

What You Bring

Required

• 3–7 years of experience in data engineering or a closely related discipline
• Strong SQL proficiency
• Hands-on experience with a cloud data warehouse, a transformation/modeling framework, and a workflow orchestration tool
• Ability to work with non-technical stakeholders and translate business needs into technical requirements
• Clear written and verbal communication, including documentation others can use without a follow-up conversation

Strongly Preferred

• Snowflake, dbt, and Dagster
• AWS familiarity (storage, IAM, basic networking)

Nice to Have

• Python for pipeline development or scripting
• Data quality frameworks, data contracts, or observability tooling
• Experience supporting ML pipelines or AI data preparation
• Medallion or similar layered architecture
• Data governance or data catalog experience

Tech Environment: Snowflake, Dagster, dbt, AWS (S3, EC2, IAM), Python, GitHub

Working Conditions

On site at corporate headquarters, collaborating in person with technology and business teams. Not remote. Minimal travel.