Data Engineer & ETL Developer

About DAT

DAT is an award-winning employer of choice and a next-generation SaaS technology company that has been at the leading edge of innovation in transportation supply chain logistics for 45 years. We continue to transform the industry year over year, by deploying a suite of software solutions to millions of customers every day - customers who depend on DAT for the most relevant data and most accurate insights to help them make smarter business decisions and run their companies more profitably. We operate the largest marketplace of its kind in North America, with 400 million freights posted in 2022, and a database of $150 billion of annual global shipment market transaction data. Our headquarters are in Denver, CO, and Beaverton, OR, with additional offices in Seattle, WA; Springfield, MO; and Bangalore, India. For additional information, see www.DAT.com/company

 

Job Application Deadline: 03/31/2026

 

The Opportunity

DAT is looking for a Data Engineer/ETL Developer to join our Data Engineering team working hybrid in Denver, Colorado.

What You’ll Do

Data Pipeline Development & Engineering

• ELT Process Implementation: Assist senior engineers in designing, building, testing, and maintaining cloud-native ELT (Extract, Load, Transform) data pipelines, ensuring data is reliably loaded into Snowflake.
• Transformation with dbt: Develop and maintain data models using dbt (data build tool) for data cleaning, aggregation, and transformation and utilize the Snowflake data warehouse.
• SQL Development: Write, optimize, and review complex SQL queries for data manipulation, transformation, and performance tuning on Snowflake.
• Python Scripting: Utilize Python to build custom data extraction scripts, implement monitoring tools, and contribute to general automation efforts.

Workflow Orchestration and Automation

• Airflow DAGs: Learn to author, schedule, and monitor data workflows defined as Directed Acyclic Graphs (DAGs) in Apache Airflow.
• Pipeline Scheduling: Integrate and orchestrate dbt runs and other pipeline tasks within Airflow to manage dependencies and execution timing.
• Automation: Focus on automating repetitive tasks across the data lifecycle, reducing manual effort and improving pipeline efficiency.

Data Quality, Testing, and Monitoring

• • Data Quality: Implement data validation and testing frameworks using features of dbt (e.g., uniqueness, non-null checks) to ensure high data quality and accuracy within Snowflake data marts.
  • Troubleshooting: Monitor data pipeline health, troubleshoot failed Airflow tasks and dbt runs, and quickly resolve data flow issues.
  • Documentation: Maintain clear and current technical documentation for data models, Airflow DAGs, and pipeline logic.

Key Technologies We Use

• Data Warehouse: Snowflake
• Transformation: dbt (data build tool)
• Orchestration: Apache Airflow
• Programming: Python, SQL

The Skills and Experience You’ll Bring

• Experience: 3-5 years of professional experience in a Data Engineering, Analytics Engineering, or similar technical role (including relevant internship experience).
• Education: Bachelor's Degree in Computer Science, Information Technology, Engineering, or a related quantitative field.
• Technical Proficiency:
• Required: Strong proficiency in Python and advanced knowledge of SQL.
• Hands-on experience with a cloud data warehouse, preferably Snowflake.
• Familiarity with data transformation concepts and tools, specifically dbt.
• Basic experience creating or running jobs/workflows