What are the responsibilities and job description for the Data Solution Architect – Snowflake & AWS position at Staffingine LLC?
Staffingine LLC is excited to announce an excellent Contract opportunity with one of our top clients!
Position: Data Solution Architect – Snowflake & AWS
Location: Peoria, IL – Onsite 5 Days/Week (Non-Negotiable)
Experience: 15 Years
Technical Focus: 70% Snowflake | 30% AWS
Leadership: Onshore & Offshore Team Management
Job Summary
- We are seeking a Senior Solution Architect with 15 years of experience in data engineering, data warehousing, or enterprise architecture. The ideal candidate will have strong expertise in Snowflake, AWS, and enterprise data platforms, along with experience leading onshore and offshore teams, designing scalable data solutions, and presenting technical strategies to executive stakeholders. Manufacturing domain experience is required.
Key Capabilities
- Experience with designing & delivering Enterprise data Platforms and awareness of all the areas of the data ecosystem (Data Management, Integrations (Streaming/Batch), Visualizations, Analytical Platforms etc.)
- Manufacturing domain experience
- Design Architectures: Build secure, scalable data solutions on AWS and Snowflake
- Lead Teams: Lead Onshore & Offshore teams to create the solution
- Drive Innovation: Apply AI awareness to increase productivity and develop AI solutions.
- Lead Readouts: Ability to present technical plans and business value to executive stakeholders clearly.
- Communication: Exceptional verbal and written communication skills for high-level leadership readouts.
Detailed Technical Skills
- Cloud Infrastructure (AWS): Deep expertise in IAM, VPC networking, CloudWatch, S3, IAM, Lambda, Glue, and EMR.
- Data Warehousing (Snowflake): Mastery of Snowpipe, streams, tasks, dynamic tables, data sharing, and storage optimization.
- Development & Scripting (Python): Advanced skills in PySpark, Pandas, NumPy, and building optimized, object-oriented data frameworks.
- DevOps & CI/CD: Hands-on experience with Git, GitHub Actions, GitLab CI, and Infrastructure as Code (Terraform or CloudFormation).
- AI & Machine Learning Awareness: Awareness of vector databases, LLM integration, prompt engineering, and MLOps pipelines (e.g., SageMaker).
- Data Governance & Security: Proven ability to implement role-based access control (RBAC), data masking, and encryption at rest/in transit.
Required Qualifications
- Experience: 15 years of proven experience in data engineering, data warehousing, or enterprise architecture roles