Stott and May

Senior Data Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Engineer on a 6-month rolling contract, fully remote (CST time zone), focusing on improving data workflows in manufacturing. Key skills include Snowflake, dbt, Python, Airflow, and Infrastructure as Code.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
760
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πŸ—“οΈ - Date
March 24, 2026
πŸ•’ - Duration
More than 6 months
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🏝️ - Location
Remote
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πŸ“„ - Contract
W2 Contractor
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
Chicago, IL
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🧠 - Skills detailed
#Observability #Data Engineering #Infrastructure as Code (IaC) #Logging #Monitoring #dbt (data build tool) #Python #Airflow #Snowflake
Role description
Data Engineer Contract Opportunity (Manufacturing | North America) We're supporting a leading North American manufacturing company on a data engineering engagement focused on strengthening the orchestration and reliability of their modern data platform. The environment includes Snowflake and dbt, but this role is focused on the platform layer that sits underneath the pipelines. This is a chance to improve how data workflows are scheduled, monitored and operated within a large enterprise environment: β€’ Improve orchestration and workflow management using Airflow or Dagster β€’ Build and manage infrastructure as code supporting data workflows β€’ Implement observability across pipelines including logging, monitoring and alerting β€’ Improve reliability and scheduling of production data workloads β€’ Support a modern data stack built on Snowflake, dbt and Python Contract details: β€’ Full-time (40 hrs/week) β€’ Start ASAP β€’ 6-month rolling contract (will likely go for well over 12 months!) β€’ Competitive hourly rates (W2 or your own LLC) β€’ Fully remote, US-based, CST time zone You’ll join an existing team helping define best practices for orchestration, platform reliability and data workflow operations. Initial 3-month contract with strong likelihood of long-term extension. Greenfield-style platform improvements inside a well-established manufacturing organization. If you enjoy improving the foundations that keep data platforms reliable and observable in production, this one’s worth applying to.