Brooksource
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with a contract length of "unknown" and a pay rate of "$XX/hour." The position requires expertise in SQL, Python, AWS, Snowflake, ETL/ELT frameworks, and AI-enabled tools, along with experience in Agile methodologies.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
480
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🗓️ - Date
August 20, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Charlotte, NC
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🧠 - Skills detailed
#Data Warehouse #Scala #Scrum #"ETL (Extract #Transform #Load)" #Python #Code Reviews #Data Ingestion #Security #S3 (Amazon Simple Storage Service) #DevOps #Observability #Snowflake #Data Quality #Data Engineering #AWS (Amazon Web Services) #Batch #Cloud #GitLab #Automation #Data Lake #Data Processing #Data Modeling #Data Pipeline #SQL (Structured Query Language) #Documentation #Lambda (AWS Lambda) #Agile #Storage #Deployment #Datasets #AI (Artificial Intelligence) #Automated Testing
Role description
Design and Build Data Solutions
• Design, develop, and maintain scalable batch and near real-time data pipelines using SQL and Python.
• Build, optimize, and support data ingestion, transformation, and orchestration processes in Snowflake and AWS.
• Develop reusable data assets, curated datasets, and data models that support analytics, reporting, operational workflows, and AI solutions.
• Create and maintain ETL/ELT frameworks to integrate data from multiple source systems.
• Ensure data solutions are scalable, reliable, secure, and cost-effective.
Cloud Data Engineering
• Leverage AWS services such as S3, Lambda, Glue, ECS, and other cloud-native technologies to enable enterprise data processing and storage.
• Support cloud data warehouse and data lake architectures.
• Monitor, tune, and optimize data workloads to improve performance, reliability, and cost efficiency.
• Understand data quality, governance, lineage, and observability capabilities across data products and platforms.
AI Enabled Engineering Productivity
• Leverage AI powered development tools and coding assistants to improve engineering productivity, accelerate software delivery, and enhance code quality.
• Utilize generative AI capabilities to support code generation, documentation creation, testing, troubleshooting, and data pipeline development.
• Identify opportunities to automate manual engineering processes through AI-enabled workflows and tooling.
• Evaluate and adopt emerging AI technologies and best practices while adhering to enterprise security, governance, and responsible AI standards.
DevOps & Engineering Excellence
• Utilize GitLab for source control, CI/CD pipelines, automated testing, code reviews, and deployment automation.
• Implement DevOps best practices to improve delivery speed, quality, reliability, and operational support.
• Participate in production support, incident management, root cause analysis, and continuous improvement activities.
• Develop and maintain technical documentation, standards, and reusable engineering components.
Agile Delivery & Collaboration
• Participate in Agile Scrum ceremonies including sprint planning, backlog refinement, daily standups, sprint reviews, and retrospectives.
• Collaborate with cross-functional teams to translate business requirements into scalable technical solutions.
• Contribute to architecture, data modeling, and design discussions.
Design and Build Data Solutions
• Design, develop, and maintain scalable batch and near real-time data pipelines using SQL and Python.
• Build, optimize, and support data ingestion, transformation, and orchestration processes in Snowflake and AWS.
• Develop reusable data assets, curated datasets, and data models that support analytics, reporting, operational workflows, and AI solutions.
• Create and maintain ETL/ELT frameworks to integrate data from multiple source systems.
• Ensure data solutions are scalable, reliable, secure, and cost-effective.
Cloud Data Engineering
• Leverage AWS services such as S3, Lambda, Glue, ECS, and other cloud-native technologies to enable enterprise data processing and storage.
• Support cloud data warehouse and data lake architectures.
• Monitor, tune, and optimize data workloads to improve performance, reliability, and cost efficiency.
• Understand data quality, governance, lineage, and observability capabilities across data products and platforms.
AI Enabled Engineering Productivity
• Leverage AI powered development tools and coding assistants to improve engineering productivity, accelerate software delivery, and enhance code quality.
• Utilize generative AI capabilities to support code generation, documentation creation, testing, troubleshooting, and data pipeline development.
• Identify opportunities to automate manual engineering processes through AI-enabled workflows and tooling.
• Evaluate and adopt emerging AI technologies and best practices while adhering to enterprise security, governance, and responsible AI standards.
DevOps & Engineering Excellence
• Utilize GitLab for source control, CI/CD pipelines, automated testing, code reviews, and deployment automation.
• Implement DevOps best practices to improve delivery speed, quality, reliability, and operational support.
• Participate in production support, incident management, root cause analysis, and continuous improvement activities.
• Develop and maintain technical documentation, standards, and reusable engineering components.
Agile Delivery & Collaboration
• Participate in Agile Scrum ceremonies including sprint planning, backlog refinement, daily standups, sprint reviews, and retrospectives.
• Collaborate with cross-functional teams to translate business requirements into scalable technical solutions.
• Contribute to architecture, data modeling, and design discussions.
