Data Engineer: AI-Ready Data Pipelines

Koch Business Solutions, LP; Wichita (KS); On-site; USD 110,000 - 140,000; Full time

Job summary

Koch Engineered Solutions seeks a Data Engineer to own data products that power analytics, reporting, and AI across the business. Based in Wichita, KS, you will design, build, and maintain ELT pipelines, partnering with stakeholders to ensure data models reflect real-world operations while maintaining high quality.

You will apply modern data engineering practices, contribute to platform reliability, and use tools like Snowflake, dbt, and Git in a collaborative, fast-moving environment.

Qualifications

• Hands-on experience building data pipelines in cloud data warehouse environments.
• SQL and experience with Snowflake, Redshift, BigQuery, or Databricks.
• Experience using dbt or equivalent transformation framework with version control and testing.
• Knowledge of Git, CI/CD, and modern software engineering practices.
• Experience translating business processes into scalable data structures.
• Ability to partner with stakeholders to define requirements and ensure data accuracy.

Responsibilities

• Own data product delivery and ensure reliability and maintainability.
• Build and maintain ELT pipelines across staging, intermediate, and mart layers.
• Collaborate with stakeholders to validate business logic and data models.
• Integrate data from multiple ERP systems, APIs, and cloud feeds.
• Structure data to be AI-ready with clear lineage and quality controls.
• Contribute to platform reliability with monitoring, automation, and support.

Skills

Data pipelines

SQL

dbt

Git & CI/CD

Dimensional modeling

Stakeholder collaboration

Education

Bachelor's degree in a relevant field

Tools

Snowflake

Redshift

BigQuery

Databricks

AI tools

Job description

Koch Engineered Solutions seeks a Data Engineer to own data products that power analytics, reporting, and AI across the business. Based in Wichita, KS, you will design, build, and maintain ELT pipelines, partnering with stakeholders to ensure data models reflect real-world operations while maintaining high quality.

You will apply modern data engineering practices, contribute to platform reliability, and use tools like Snowflake, dbt, and Git in a collaborative, fast-moving environment.