Data Engineer

Your Job

Koch Engineered Solutions (KES) is currently looking for a Data Engineer to join our global Information Technology (IT) capability. This role owns the delivery and ongoing success of reliable, scalable data products that support analytics, reporting, and AI solutions across KES business units. You will partner directly with business stakeholders and Data Architects to translate real-world processes and complex source data into production-ready solutions.

Success in this role requires engineering discipline, curiosity about how the business operates, clear stakeholder communication, and a commitment to continuously improving the modern data platform.

This position is based in Wichita, KS, and is not eligible for VISA Sponsorship.

The IT capability is a vital component of the KES strategy to improve business performance through the application of technology and to profitably transform our business. Our team operates with a startup mentality - working as an integrated group with the Engineering, Operations, Commercial, and Finance teams to design, build, and scale innovative solutions that transform KES work processes.

• Own the delivery and ongoing success of data products, ensuring they solve the intended business problem and remain reliable, usable, and maintainable in production.
• Build and maintain ELT pipelines across staging, intermediate, and mart layers using modular engineering practices, automated testing, and production-ready deployment processes.
• Engage directly with business stakeholders, including cost accountants, project managers, and analysts, to understand their processes, validate business logic, and ensure data models accurately represent real-world operations.
• Integrate data from diverse and sometimes complex 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 through semantic clarity, consistent naming, documented lineage, and quality sufficient for both human analysts and AI/LLM consumption.
• Apply AI-assisted engineering tools, such as Claude Code and Snowflake Cortex, to accelerate delivery, improve code quality, and identify opportunities to embed AI capabilities into data products.
• Establish risk-based data quality controls that validate critical business rules, detect upstream issues early, and prevent unreliable data from reaching consumers.
• Contribute to platform reliability by incorporating monitoring, alerting, automation, and operational support into delivered solutions.
• Participate in legacy platform migration, transitioning workloads to the modern data stack while preserving data integrity and business continuity.
• Strengthen team capability through code reviews, pairing, documentation, and reusable engineering patterns that improve consistency and reduce key-person dependencies.

• Hands-on experience building data pipelines in a cloud data warehouse environment.
• SQL skills and experience with cloud data platforms such as Snowflake, Redshift, Big Query, or Databricks.
• Experience using dbt or a comparable transformation framework that supports version control, testing, and modular design.
• Working knowledge of cloud services and modern software engineering practices, including Git and CI/CD.
• Experience applying dimensional data modeling and translating business processes into scalable data structures.
• Experience partnering directly with business stakeholders to define requirements, validate business logic, and ensure data accuracy.

• 3+ years of experience building data pipelines in a cloud data warehouse environment.
• Experience using AI-assisted development tools to improve engineering productivity and solution quality.
• Experience integrating data across multiple ERPs or source systems, including entity resolution and reconciliation.
• Python experience supporting data engineering automation, orchestration, or data applications.
• Experience designing data products for analytics, AI/ML, or LLM consumption.
• Experience with Power BI data modeling, semantic layer design, or business-facing analytics solutions.

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