Senior Data/AI Engineer

Charter Next Generation•Chicago, IL

4d•Hybrid

About The Position

At CNG, we are owners. We empower every individual to make a significant impact — because every moment matters. Whether you’re starting your career journey or aiming for the next level, we foster a supportive environment where your contributions are valued and celebrated. Why Join CNG? Employee Ownership: Every employee receives equity shares, enabling you to participate in our company's growth and prosperity. Work-Life Balance: We support true work-life integration for all CNG employee owners. Career Growth: Advance your career within our expanding network of manufacturing facilities with comprehensive training and development programs. Purpose-Driven Work: Help create sustainable packaging solutions that reduce food waste and protect our environment for future generations. The Role From supporting internal teams to developing next-gen materials, our professional roles span strategy, innovation, and purpose. At CNG, you’ll do work that matters—and own it all. Schedule: Hybrid, Business Hours

Requirements

5+ years of experience in data engineering, machine learning engineering, software engineering, analytics engineering, or a related technical field.
Strong Python development experience.
Advanced SQL skills and experience working with analytical data models.
Experience building ETL/ELT pipelines and integrating multiple data sources.
Experience with modern cloud data platforms.
Experience developing or integrating REST APIs.
Strong understanding of software engineering practices including Git, automated testing, CI/CD, and code review.
Demonstrated ability to take technical solutions from development through production.

Responsibilities

Design, build, test, and maintain scalable data pipelines and analytical data products.
Develop data transformations and models using SQL, Python, Snowflake, and dbt.
Integrate data from ERP, manufacturing, supply chain, commercial, finance, and other enterprise systems.
Build Python applications, APIs, and data services using frameworks such as FastAPI.
Productionize machine learning, forecasting, optimization, and AI solutions.
Develop batch and real-time patterns for integrating data and models with downstream applications.
Build and maintain automated testing, monitoring, and data-quality controls.
Implement CI/CD and modern software engineering practices for data and AI solutions.
Use Infrastructure as Code and automation to create repeatable, maintainable environments and deployments.
Partner with data scientists, analysts, engineers, and business stakeholders to translate business requirements into scalable technical solutions.
Support ML lifecycle management including deployment, monitoring, versioning, and retraining.
Help develop reusable engineering patterns for Generative AI and AI-enabled applications.
Diagnose and resolve performance, reliability, and data-quality issues across production systems.
Contribute to code reviews, technical standards, documentation, and mentoring across the team.