01Key Responsibilities
ETL Pipeline Development: Design, develop, and maintain ETL processes using Python, AWS Glue, or other serverless technologies to ingest data from various sources (databases, APIs, files), transform it into a usable format, and load it into data warehouses or data lakes.
AWS Serverless Expertise: Leverage AWS services such as AWS Lambda, AWS Step Functions, AWS Glue, AWS S3, and AWS Redshift to build serverless data pipelines that are scalable, reliable, and cost-effective.
Data Modeling: Collaborate with data scientists and analysts to understand data requirements and design appropriate data models, ensuring data is structured optimally for analytical purposes.
Data Quality Assurance: Implement data validation and quality checks within ETL pipelines to ensure data accuracy, completeness, and consistency.
Performance Optimization: Continuously optimize ETL processes for efficiency, performance, and scalability, monitoring and troubleshooting any bottlenecks or issues that may arise.
Documentation: Maintain comprehensive documentation of ETL processes, data lineage, and system architecture to ensure knowledge sharing and compliance with best practices.
Security and Compliance: Implement data security measures, encryption, and compliance standards (e.g., GDPR, HIPAA) as required for sensitive data handling.
Monitoring and Logging: Set up monitoring, alerting, and logging systems to proactively identify and resolve data pipeline issues.
Collaboration: Work closely with cross-functional teams, including data scientists, data analysts, software engineers, and business stakeholders, to understand data requirements and deliver solutions.
Continuous Learning: Stay current with industry trends, emerging technologies, and best practices in data engineering and cloud computing and apply them to enhance existing processes.
Qualifications:
Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
Proven experience as a Data Engineer with a focus on ETL pipeline development.
Strong proficiency in Python programming.
In-depth knowledge of AWS serverless technologies and services.
Familiarity with data warehousing concepts and tools (e.g., Redshift, Snowflake).
Experience with version control systems (e.g., Git).
Strong SQL skills for data extraction and transformation.
Excellent problem-solving and troubleshooting abilities.
Ability to work independently and collaboratively in a team environment.
Effective communication skills for articulating technical concepts to non-technical stakeholders.
Certifications such as AWS Certified Data Analytics - Specialty or AWS Certified DevOps Engineer are a plus.
Preferred Experience: .
