Staff Data Engineer

DAT•Denver, CO

3d•$134,000 - $175,000•Hybrid

About The Position

DAT Freight & Analytics is an award-winning employer of choice and a next-generation SaaS technology company that has been at the leading edge of freight and logistics innovation for nearly five decades. Founded in 1978, DAT operates the largest freight marketplace in North America — processing 250 million+ load posts annually and maintaining one of the largest repositories of freight market transaction data in the world. On a defined path to $1 billion in revenue, DAT deploys a suite of software solutions, machine learning models, and intelligent automation tools that help brokers, carriers, and shippers price freight accurately, source capacity, reduce risk, and operate more efficiently. With nearly 700 teammates across offices in Denver, CO; Portland, OR; Seattle, WA; Springfield, MO; Toronto, ON; and Bangalore, India, DAT combines the credibility of a multi-decade market leader with the drive of a company that is not done disrupting the industry it helped build. For more information, visit www.DAT.com

Requirements

7+ years of relevant professional engineering experience, with a proven track record of building and operating large-scale distributed systems.
Bachelor’s degree in Computer Science, Computer Engineering, or a related technical discipline.
Strong data engineering background working with dbt, Snowflake, Kafka, and cloud-based services (AWS S3, EMR, Glue).
Hands-on experience with Terraform, Airflow, Tableau, MySQL, and Oracle.
Deep knowledge of professional software engineering practices for the full technology management life cycle; experience with Agile is preferred.
Excellent verbal and written communication skills with an interest in continuous learning and improvement.

Responsibilities

Lead the design and evolution of our modern data platform architecture, ensuring it remains scalable, available, and performant.
Apply in-depth knowledge of advanced architectural patterns—such as Event Driven, CQRS, event sourcing, and Saga patterns—to solve complex data challenges.
Develop, document, and communicate standard methods and processes to be utilized within our technology stack, including dbt, Airflow, Snowflake, Kafka, and traditional ETL.
Act as a key partner to senior leadership on strategic data projects and build strong working relationships with other IT leaders by providing technical acumen and thought leadership.
Architect and manage enterprise-scale data platforms while setting best practices for development teams, including security, privacy, monitoring & alerting, and CI/CD.
Implement sophisticated data modeling strategies, including Kimball, Inmon, or Data Vault, and drive the transition toward modern concepts like Data Mesh.
Provide solutions for complex business problems, enabling insights that can empower better decision-making across the organization.
Set and enforce best practices for the full software development and technology management life cycle, including coding standards, code reviews, source control management, and testing.
Take an active role in fostering a culture of learning and excellence within the engineering organization.
Leverage data to prioritize work and influence others across the organization.
Proactively implement change and improvements when seeing opportunities to optimize the platform or processes.