Data Engineer

Create trusted, scalable data foundations that drive real‑time decisions on a national scale.

US Cold owns and operates one of the most complex temperature‑controlled logistics networks in North America. Every day, our systems coordinate the storage and movement of food at national scale across a network of state‑of‑the‑art distribution centers, including multiple highly automated warehouse facilities. We continue to advance our core warehouse and logistics platforms. Our current focus is on modular, event‑driven, API‑first and cloud architectures.

We continue to enhance reliability and accelerate engineering productivity by strengthening our SRE and AI practices. This is a large investment in innovation to continue to drive operational excellence at our facilities. If you want to build durable systems that operate in the physical world at scale, this is that opportunity.

We are hiring a Data Engineer to design, build, and operate scalable, reliable data pipelines and platforms that power reporting, analytics, and machine learning across the organization. This role is suited for someone with a solid foundation in modern data engineering practices who enjoys solving complex data problems and working in production environments.

You will develop and maintain data systems that ensure data is accurate, timely, and trusted.

You will work across the full data lifecycle—from ingestion and transformation to validation, deployment, and monitoring.

This role offers the opportunity to build a strong medallion data foundation—delivering reliable analytics‑ and AI‑driven systems where data quality, uptime, and operational impact truly matter.

• Design, build, and operation of batch and streaming data pipelines.
• Ingestion and processing of structured and semi-structured data from sources such as databases, APIs, event streams, logs, and files.
• Data quality, reliability, and observability across data pipelines and platforms.
• Development of clean, maintainable SQL and Python code for data transformations.
• Delivery of high‑quality, well‑modeled datasets that support analytics and data science teams.
• Application of best practices in data modeling, testing, version control, and documentation.
• Leading and participating in code reviews and technical design discussions to ensure scalable and reliable solutions.
• Staying current with modern data technologies and AI-enabled data workflows and applying them where they add value.

• Multi‑facility, high‑availability Warehouse management and logistics systems
• Migration toward cloud‑native, event‑driven architectures
• Azure cloud‑native services
• ADF, Python and Azure functions to build data movement pipelines
• Snowflake for real‑time data warehousing
• CI/CD‑driven delivery model
• Power BI for data visualization
• Application of low‑code platforms for process automations
• AI mindset with application of data science models for warehouse optimization, routing, consolidation, and capacity planning

• Professional experience building and supporting data solutions in production environments
• Strong fundamentals in SQL (joins, aggregations, window functions) and Python or a similar programming language
• Solid understanding of core data engineering concepts, including data pipelines and ETL/ELT patterns using tools such as ADF or Informatica
• Experience with data modeling concepts, such as fact and dimension tables
• Understanding of batch and streaming data processing paradigms
• Familiarity with cloud data ecosystems (AWS, Azure, or GCP), with hands‑on or practical experience
• Strong problem‑solving skills, curiosity, and a continuous‑learning mindset

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Engineering, Mathematics, or a closely related discipline.

• Hands‑on experience (academic or professional) with:
• Snowflake, Big Query, Redshift, or similar data warehouses
• ETL tools such as ADF, Informatica
• Dbt or transformation‑as‑code frameworks
• Exposure to:
• Machine learning pipelines or AI‑driven data products
• Feature engineering or data preparation for ML models
• Prompt engineering, LLM pipelines,…