AWS Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AWS Data Engineer for a 6-12 month contract, remote, with a pay rate of "unknown." Key skills required include AWS, Databricks, PySpark, CDC, and Azure DevOps, with experience in data migration and optimization.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 12, 2025
🕒 - Project duration
More than 6 months
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🏝️ - Location type
Remote
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Bash #Data Pipeline #Scripting #IAM (Identity and Access Management) #DevOps #Logging #Azure DevOps #Web Services #Lambda (AWS Lambda) #Databricks #Monitoring #Data Processing #SQL (Structured Query Language) #Spark SQL #Automation #Version Control #Azure #Azure Repos #Big Data #Data Integration #Infrastructure as Code (IaC) #Python #Scala #Data Migration #Security #DMS (Data Migration Service) #Deployment #RDS (Amazon Relational Database Service) #GIT #Terraform #AWS (Amazon Web Services) #Databases #Data Analysis #AWS CloudWatch #Cloud #Data Engineering #S3 (Amazon Simple Storage Service) #Database Migration #VPC (Virtual Private Cloud) #Migration #Spark (Apache Spark) #PySpark #EC2 #AWS DMS (AWS Database Migration Service)
Role description
Role - AWS Data Engineer
Duration - 6 – 12 months
Remote / EST hours
Proficient in AWS, Databricks, and Azure DevOps, with a focus on strong analytical skills in PySpark, Delta Live Tables, Change Data Capture (CDC), and on-premises to AWS data migration.
Technical Skills
AWS (Amazon Web Services):
• Core Services: Proficiency with core AWS services like EC2, S3, RDS, Lambda, and VPC.
• Data Services: Experience with AWS data services Glue and EMR.
• AWS DMS: Knowledge of AWS Database Migration Service (DMS) for migrating databases to AWS.
• CDC: Understanding of Change Data Capture (CDC) techniques to capture and replicate changes from source databases to target databases.
• Security: Understanding of AWS security best practices, IAM, and encryption.
Databricks:
• PySpark & Spark SQL: Strong analytical skills in PySpark & Spark SQL for big data processing and analysis.
• Delta Live Tables: Expertise in using Delta Live Tables for building reliable and scalable data pipelines.
• Notebooks: Strong utilization of Databricks Notebooks for data analysis.
• Workflows : Setting up and monitoring Databricks Workflows.
• Data Integration: Experience integrating Databricks with AWS services.
DevOps Principles:
• CI/CD Pipelines: CI/CD pipelines using Azure Pipelines.
• Version Control: Proficiency with Azure Repos and Git for version control.
• Automation: Scripting and automation using PowerShell, Bash, or Python. Automating the build, test, and deployment processes
Infrastructure as Code (IaC):
• Terraform: Experience with Terraform for managing AWS and Azure infrastructure.
On Prem integration with AWS
• Integrating on prem data with AWS and Databricks.
• Thoroughly test and validate the data to ensure it has been transferred correctly and is fully functional.
Optimization and Monitoring:
• Optimize AWS services and Databricks for performance and cost-efficiency.
• Proficiency in setting up monitoring and logging using tools like AWS CloudWatch to track the performance and health of the complete data flow.
Role - AWS Data Engineer
Duration - 6 – 12 months
Remote / EST hours
Proficient in AWS, Databricks, and Azure DevOps, with a focus on strong analytical skills in PySpark, Delta Live Tables, Change Data Capture (CDC), and on-premises to AWS data migration.
Technical Skills
AWS (Amazon Web Services):
• Core Services: Proficiency with core AWS services like EC2, S3, RDS, Lambda, and VPC.
• Data Services: Experience with AWS data services Glue and EMR.
• AWS DMS: Knowledge of AWS Database Migration Service (DMS) for migrating databases to AWS.
• CDC: Understanding of Change Data Capture (CDC) techniques to capture and replicate changes from source databases to target databases.
• Security: Understanding of AWS security best practices, IAM, and encryption.
Databricks:
• PySpark & Spark SQL: Strong analytical skills in PySpark & Spark SQL for big data processing and analysis.
• Delta Live Tables: Expertise in using Delta Live Tables for building reliable and scalable data pipelines.
• Notebooks: Strong utilization of Databricks Notebooks for data analysis.
• Workflows : Setting up and monitoring Databricks Workflows.
• Data Integration: Experience integrating Databricks with AWS services.
DevOps Principles:
• CI/CD Pipelines: CI/CD pipelines using Azure Pipelines.
• Version Control: Proficiency with Azure Repos and Git for version control.
• Automation: Scripting and automation using PowerShell, Bash, or Python. Automating the build, test, and deployment processes
Infrastructure as Code (IaC):
• Terraform: Experience with Terraform for managing AWS and Azure infrastructure.
On Prem integration with AWS
• Integrating on prem data with AWS and Databricks.
• Thoroughly test and validate the data to ensure it has been transferred correctly and is fully functional.
Optimization and Monitoring:
• Optimize AWS services and Databricks for performance and cost-efficiency.
• Proficiency in setting up monitoring and logging using tools like AWS CloudWatch to track the performance and health of the complete data flow.
