Data Engineer

Your Job

Koch Engineered Solutions (KES) is currently looking for a Data Engineer to work in our Information Technology (IT) Team at our Wichita, KS headquarters.

The IT team is a vital component in KES' strategy to improve business performance through the application of technology to profitably transform our business.

Working as an integrated group with the Engineering, Operations, Commercial and Financial teams that design and maintain facilities.

Our team functions as a central capability within our enterprise to develop innovative solutions to transform KES work processes.

Our Team

As a member of the Information Technology team, you will need to thrive in a fast pace and innovative environment.

You will collaborate to develop solutions and prove their value through experimentation and scalable deployment in our business.

Collaboration, creativity, and focus on attaining positive business results will be necessary skills.

Like an employee of a startup, you will need to be resourceful and capable of partnering with market solution providers that can accelerate progress of our business objectives.

What You Will Do


* Own data products end-to-end - from understanding the business problem, through source system assessment and data modeling, to production pipeline implementation in dbt and Snowflake


* Build and maintain ELT pipelines following established engineering patterns (staging, intermediate, mart layers) with automated testing, validation, and documentation that holds up in production


* Engage directly with business stakeholders - cost accountants, project managers, analysts - to understand their processes, validate business logic, and ensure your data models accurately represent the real world


* Integrate data from diverse and sometimes messy source systems, including multiple ERPs, APIs, file-based feeds, and cloud services, making deliberate decisions about entity resolution and cross-system reconciliation


* Structure data products to be AI-ready - with semantic clarity, consistent naming, documented lineage, and quality sufficient for both human analysts and AI/LLM consumption


* Use AI tools as a natural part of your engineering workflow (Claude Code, Snowflake Cortex, and others) to accelerate development, improve code quality, and evaluate where AI capabilities can be embedded into the data products you build


* Develop and maintain data quality frameworks - not just running tests, but deciding what to test, identifying where upstream business processes create risk, and building detection that catches problems before consumers do


* Contribute to platform reliability by building monitoring, alerting, and automation into your work rather than treating it as someone else's problem


* Participate in legacy platform migration, transitioning workloads from older tools to the modern stack while preserving data integrity and business continuity


* Share knowledge through code review, docume...