AWS Solutions Architect, Snowflake Solutions Architect
- Talentburst
96 Days Ago
NA
NA
Portland-OR
5-7 Years
Required Skills: loud platform (AWS, Snowflake), DevOps, ML , Data Ops,AWS architecture along with AWS Services hands on knowledge, Architecture and Solution Assessment, Critical thinking and problem-solving skills
Job Description
Title: AWS / Snowflake Solutions Architect
Duration: 6 Months with possible extension
Job Description:
Tools/ Technology: Cloud platform (AWS, Snowflake), DevOps, ML , Data Ops
Top 3 Must-Haves (Hard and/or Soft Skills):
1. AWS architecture along with AWS Services hands on knowledge
2. Architecture and Solution Assessment
3. Critical thinking and problem-solving skills
Top 3 Nice-To-Haves (Hard and/or Soft Skills)
1.Working experience of Cloud architectural patterns and frameworks
2. In-depth knowledge of AWS services, especially IAM, EC2, S3, RDS, and Sage Maker
3. DevOps/ML Ops Automation (Terraform, CloudFormation)
Certification Requirements (Any Preferences): AWS Certified Solutions Architect, Snowflake
How many years of experience are you looking for?: 5-7 year’s experience
Seeking a skilled Senior Solutions architect with expertise in designing and implementing cloud-based data solutions. The ideal candidate will have a strong background in cloud infrastructure, data warehousing, machine learning operations, and automation practices.
Key Responsibilities/Skills:
• Understand current business processes and capability requirements from architecture, integrations, and process standpoint.
• Perform market analysis and short list vendors for evaluation & lead demos with vendor, bringing along business.
• Build evaluation criteria to measure vendors and put together a capability, architecture, and cost analysis for the business.
• Design and implement cloud-based (AWS - preferred) data solutions, with an emphasis on performance optimization, scalability, and security.
• Implement cloud data governance, data security, and compliance regulations.
• Apply serverless automation frameworks, Dev/ML/Data Ops tools and CI/CD pipelines.
• Bring expertise in scripting and automation tools such as Python, Bash, Terraform, or CloudFormation for cloud-based integrations.
• Evaluate and select appropriate technologies and tools.
• Work closely with cross-functional teams, including business stakeholders, data engineers, DevOps, and cloud security teams, to ensure successful migration and deployment.
• Provide technical guidance to development team and application teams to migrate data from legacy systems (e.g., DataStage) to cloud-native solutions (e.g., AWS Glue, Redshift, Snowflake)
• Define and document architectural guidelines, standards, and best practices.
• Added advantage with ETL/ELT processes, particularly in transitioning from legacy tools like DataStage to cloud-native solutions.
• Deep understanding of various data modeling techniques (e.g., dimensional modeling, Data Vault, Data Mesh) and the ability to choose the appropriate model for different business needs. Experience with conceptual, logical, and physical data modeling.
• Experience with metadata management tools and processes, including data dictionaries, business glossaries, and data lineage tracking.
• Integrate AI/ML capabilities into business applications to drive innovation and automation.
• Hands-on experience with AWS services (e.g., EC2, S3, Lambda, SageMaker,etc..)
• Excellent communication, problem-solving and team collaboration skills.
• Ensure system performance, cost efficiency, and compliance with security best practices.
Duration: 6 Months with possible extension
Job Description:
Tools/ Technology: Cloud platform (AWS, Snowflake), DevOps, ML , Data Ops
Top 3 Must-Haves (Hard and/or Soft Skills):
1. AWS architecture along with AWS Services hands on knowledge
2. Architecture and Solution Assessment
3. Critical thinking and problem-solving skills
Top 3 Nice-To-Haves (Hard and/or Soft Skills)
1.Working experience of Cloud architectural patterns and frameworks
2. In-depth knowledge of AWS services, especially IAM, EC2, S3, RDS, and Sage Maker
3. DevOps/ML Ops Automation (Terraform, CloudFormation)
Certification Requirements (Any Preferences): AWS Certified Solutions Architect, Snowflake
How many years of experience are you looking for?: 5-7 year’s experience
Seeking a skilled Senior Solutions architect with expertise in designing and implementing cloud-based data solutions. The ideal candidate will have a strong background in cloud infrastructure, data warehousing, machine learning operations, and automation practices.
Key Responsibilities/Skills:
• Understand current business processes and capability requirements from architecture, integrations, and process standpoint.
• Perform market analysis and short list vendors for evaluation & lead demos with vendor, bringing along business.
• Build evaluation criteria to measure vendors and put together a capability, architecture, and cost analysis for the business.
• Design and implement cloud-based (AWS - preferred) data solutions, with an emphasis on performance optimization, scalability, and security.
• Implement cloud data governance, data security, and compliance regulations.
• Apply serverless automation frameworks, Dev/ML/Data Ops tools and CI/CD pipelines.
• Bring expertise in scripting and automation tools such as Python, Bash, Terraform, or CloudFormation for cloud-based integrations.
• Evaluate and select appropriate technologies and tools.
• Work closely with cross-functional teams, including business stakeholders, data engineers, DevOps, and cloud security teams, to ensure successful migration and deployment.
• Provide technical guidance to development team and application teams to migrate data from legacy systems (e.g., DataStage) to cloud-native solutions (e.g., AWS Glue, Redshift, Snowflake)
• Define and document architectural guidelines, standards, and best practices.
• Added advantage with ETL/ELT processes, particularly in transitioning from legacy tools like DataStage to cloud-native solutions.
• Deep understanding of various data modeling techniques (e.g., dimensional modeling, Data Vault, Data Mesh) and the ability to choose the appropriate model for different business needs. Experience with conceptual, logical, and physical data modeling.
• Experience with metadata management tools and processes, including data dictionaries, business glossaries, and data lineage tracking.
• Integrate AI/ML capabilities into business applications to drive innovation and automation.
• Hands-on experience with AWS services (e.g., EC2, S3, Lambda, SageMaker,etc..)
• Excellent communication, problem-solving and team collaboration skills.
• Ensure system performance, cost efficiency, and compliance with security best practices.
