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Job summary
A leading consulting firm is seeking a Data Engineer to develop and manage data pipelines using AWS technologies. The candidate must have 3-5 years of experience with Python development and AWS services like Lambda and S3. The role supports a hybrid work environment, requiring collaboration with business analysts and robust problem-solving skills.
Qualifications
3-5 years of Python development experience including libraries for data processing.
Strong knowledge of AWS services and serverless architecture.
Experience building data pipelines and workflows.
Responsibilities
Develop, test, and deploy data processing pipelines using AWS technologies.
Implement ETL processes for structured and unstructured data.
Collaborate with teams to understand requirements and deliver solutions.
Skills
Python development
AWS services
Data processing (Pandas, boto3)
Version control (Git)
CI/CD practices
Tools
AWS Lambda
DynamoDB
S3
Step Functions
Job description
About the job Data Engineer | Python Development
Data Engineer with Python development experience
Experience: 3+ Years
Mode: Hybrid (2-3 days/week)
Key Responsibilities
Develop, test, and deploy data processing pipelines using AWS Serverless technologies such as AWS Lambda, Step Functions, DynamoDB, and S3.
Implement ETL processes to transform and process structured and unstructured data efficiently.
Collaborate with business analysts and other developers to understand requirements and deliver solutions that meet business needs.
Write clean, maintainable, and well-documented code following best practices.
Monitor and optimize the performance and cost of serverless applications.
Ensure high availability and reliability of the pipeline through proper design and error handling mechanisms.
Troubleshoot and debug issues in serverless applications and data workflows.
Stay up-to-date with emerging technologies in the AWS and serverless ecosystem to recommend improvements.
Required Skills and Experience
3-5 years of hands-on Python development experience, including experience with libraries like boto3, Pandas, or similar tools for data processing.
Strong knowledge of AWS services, especially Lambda, S3, DynamoDB, Step Functions, SNS, SQS, and API Gateway.
Experience building data pipelines or workflows to process and transform large datasets.
Familiarity with serverless architecture and event-driven programming.
Knowledge of best practices for designing secure and scalable serverless applications.
Proficiency in version control systems (e.g., Git) and collaboration tools.
Understanding of CI/CD pipelines and DevOps practices. Strong debugging and problem-solving skills.
Familiarity with database systems, both SQL (e.g., RDS) and NoSQL (e.g., DynamoDB).
Preferred Qualifications
Familiarity with testing frameworks (e.g., pytest) and ensuring test coverage for Python applications.
Experience with Infrastructure as Code (IaC) tools such as AWS CDK, CloudFormation.