Data Engineer

Hendrick•East Independence, MO

8d•Onsite

About The Position

The Data Engineer is a mid-level individual contributor responsible for making Hendrick Autoguard’s data reliable, accessible, and useful. The role owns the design and operation of data ingestion pipelines, transformation logic, and data models that support business intelligence and AI initiatives. The engineer is expected to understand the business and apply that context when making technical decisions, ensuring that data models accurately represent business processes as well as run reliably. The role works with analysts and BI developers, data science and AI teams, and the team administering cloud infrastructure. It owns moving data from source systems into Snowflake, including building and maintaining ingestion pipelines, orchestrating workflows in Dagster, monitoring pipeline health and data availability, investigating failures or delays, resolving root causes, and implementing alerting. The Data Engineer develops and maintains dbt transformation models that produce clean, tested, documented datasets. This includes designing dimensional models and data marts for reporting and machine learning, and applying data quality checks and testing standards. The role also prepares data for AI by structuring feature datasets and training data pipelines, collaborates with AI and data science teams on data requirements, contributes to a semantic layer for consistent access to business metrics and entities, and considers lineage and governance. The engineer advises on the direction of the data engineering toolset, evaluates platform updates and new capabilities, and keeps current with data engineering, AI data preparation, and orchestration practices. The role builds knowledge of Autoguard business processes, including vehicle service contract and protection product sales through dealership partners, claims administration, and cancellations. It engages business stakeholders to understand needs, translates them into technical designs, and uses business context to guide modeling, priorities, and quality standards. Dashboard and report preparation are not routine responsibilities, and primary ownership belongs to analysts and BI developers. The Data Engineer may occasionally help build or troubleshoot a report or assist an analyst with a source modeling issue. The core responsibility remains delivering modeled, tested data and addressing data-layer gaps exposed by reporting needs. The position is onsite at Hendrick Automotive Group headquarters in a standard office environment. Travel is minimal and may be by car or plane. The role requires prolonged sitting and extensive computer use, including typing and use of office equipment such as phones and printers.

Requirements

3–7 years of professional experience in data engineering or a closely related discipline.
Proficiency in SQL.
Hands-on experience with a cloud data warehouse, a data transformation and modeling framework, and a workflow orchestration tool.
Ability to engage with non-technical stakeholders and translate business needs into technical requirements.
Clear written and verbal communication skills.
Ability to produce accurate, specific, and usable model documentation, dataset descriptions, and design notes.
Ability to work onsite at Hendrick Automotive Group headquarters; the position is not remote.
Ability to sit for prolonged periods and use computers, typing, phones, printers, and other office equipment extensively.

Nice To Haves

Experience with Snowflake, dbt, and Dagster is strongly preferred.
Familiarity with AWS, including storage, IAM, and basic networking concepts.
Python experience for pipeline development or data scripting.
Exposure to data quality frameworks, data contracts, or observability tooling.
Experience supporting ML pipelines or AI data preparation workflows.
Familiarity with medallion architecture or similar layered data design patterns.
Involvement in data governance or data catalog efforts.

Responsibilities

Design, build, and maintain ingestion pipelines that move data from source systems into Snowflake.
Orchestrate workflows in Dagster and ensure they are observable, reliable, and operationally sound.
Monitor pipeline health and data availability; investigate and resolve failed or late loads, address root causes, and implement alerting.
Develop and maintain dbt transformation models that create clean, tested, and documented datasets.
Design dimensional models and data marts supporting reporting and machine learning use cases.
Implement data quality checks and testing standards throughout the transformation layer.
Structure feature datasets and training data pipelines for AI initiatives, and collaborate with data science and AI teams on their data requirements.
Contribute to the semantic layer for consistent access to business metrics and entities, while considering data lineage and governance.
Advise on the strategic direction of the data engineering toolset, evaluate platform updates and new capabilities, and apply current industry practices to tools and standards.
Build working knowledge of supported Autoguard business processes and engage stakeholders to translate data needs into technical designs.
Apply business context to modeling decisions, pipeline priorities, and data quality standards.
Occasionally support dashboard or report preparation, troubleshoot reports, or help analysts resolve source modeling issues.
Produce accurate, specific, usable model documentation, dataset descriptions, and design notes.
Collaborate in person with technology teams and business stakeholders.