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Koch Industries

Private conglomerate in energy and materials

AI Data Analytics Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeLisle, IL, USA

About the Job

Requirements

  • A Bachelor of Science or Master of Science in Computer Science, Data Science, Software Engineering, Applied Mathematics, or equivalent experience.
  • At least two years of experience building production software, data systems, or analytics platforms in backend, database, or data engineering.
  • Strong Python skills and experience with APIs such as FastAPI or Flask and web services.
  • Advanced SQL expertise and relational database design experience with PostgreSQL, MySQL, or SQL Server.
  • Experience with data warehousing or columnar stores such as BigQuery, Redshift, or Snowflake, or time-series databases such as TimescaleDB or InfluxDB.
  • Experience with ETL/ELT orchestration tools such as Airflow, dbt, or Spark, and incremental load strategies.
  • Hands-on experience with AI coding agents and agentic development tools such as OpenAI Codex, Claude Code, or GitHub Copilot for automated code generation, refactoring, and testing.
  • Experience designing and orchestrating agentic AI workflows involving multi-step reasoning, tool/function calling, and retrieval-augmented generation, using frameworks such as LangChain, LangGraph, AutoGen, or CrewAI.
  • Experience with containerization and orchestration using Docker or Kubernetes, and CI/CD tools such as GitHub Actions or Jenkins.
  • Experience building dashboards and visualizations using Power BI, Tableau, Dash, or React.
  • Strong software engineering practices, including unit and integration testing, code reviews, continuous integration, and documentation.
  • Excellent communication and cross-functional collaboration skills.

Responsibilities

  • Translate engineering problems into scalable, data-driven applications and services that integrate with internal systems.
  • Design, implement, and maintain relational and analytical databases, including logical and physical schema design, indexing, partitioning, backups, and migrations.
  • Build reliable ETL/ELT pipelines and data ingestion systems for heterogeneous enterprise sources, including MES, ERP/SAP, CRM, warranty, and field records.
  • Develop backend services and REST/gRPC APIs to serve analytics, AI agent outputs, and application data to dashboards, lab tools, and internal applications.
  • Implement frontend dashboards and interactive user interfaces using tools such as Power BI, Tableau, Dash, or React to present insights and support engineer workflows.
  • Design, build, and orchestrate multi-step, tool-using agentic AI workflows to automate engineering tasks such as code generation, code review, data analysis, testing, and reporting; use AI coding agents such as OpenAI Codex or Claude Code to accelerate development.
  • Ensure application reliability, observability, security—including logging, metrics, role-based access control, and encryption at rest and in transit—and data governance.
  • Implement CI/CD for code, database schema migrations, and agentic workflow deployment; enforce automated testing and rollback strategies.
  • Optimize query performance and schemas for analytics workloads and high-ingest scenarios.
  • Produce architecture documentation, data contracts, and operational runbooks; enable knowledge transfer and training for stakeholders.

Desired Qualifications

  • Experience with message brokers or streaming technologies such as Kafka or RabbitMQ for event-driven agentic pipelines.
  • Experience with prompt engineering, agent evaluation/observability tools such as LangSmith or Helicone, and guardrails for safe, reliable autonomous agent behavior.
  • Contributions to open-source agentic frameworks, or personal/professional projects demonstrating autonomous coding agents, agent-to-agent orchestration, or AI-driven developer tooling.
  • Experience with database migration tooling and schema versioning such as Flyway or Alembic.
  • Familiarity with cloud-native data architectures on AWS, GCP, or Azure, and security/compliance practices for proprietary data.
  • Experience building custom AI agent tools and integrations, such as MCP servers or plugin/extension architectures, to connect agentic workflows with enterprise systems such as SAP/xRPM, Power Automate, or Salesforce.

About the Company

Koch Industries

Private conglomerate in energy and materials

Koch Industries is a large, private, diversified conglomerate that operates in energy, chemical processing, and manufacturing. It started from an innovation that turned heavy crude oil into gasoline and grew through long‑term investments rather than short‑term market moves. The company generates value by refining fuels and producing chemical products, and it operates Georgia-Pacific, a major maker of paper and building products. Its business model relies on reinvesting most earnings back into the company to fund steady, long‑term growth rather than paying out frequent dividends or seeking quick profits. Koch differentiates itself from many competitors by staying privately owned, which reduces pressure from public markets and enables longer‑range planning. The company’s goal is to build scale and durability across its holdings by sustainable, multi‑decade growth rather than rapid, episodic gains.

Company size
5,001-10,000
Company stage
Growth Equity (Non-Venture Capital)
Total funding
$985.2M
Headquarters
Wichita, KS
Founded
1940

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