AWS Data Engineer
Job Description
We are seeking an experienced AWS Data Engineer to join our team at Qode. This role focuses on designing and implementing large-scale data solutions using AWS services and modern data engineering tools. The ideal candidate will work with Python, PySpark, and various AWS services to build robust data pipelines and platforms. You'll be responsible for creating ETL/ELT workflows, implementing data lake architectures, and ensuring data quality and security. This position offers the opportunity to work with cutting-edge technologies and collaborate with cross-functional teams to deliver impactful data solutions. The role requires expertise in distributed systems, cloud computing, and data architecture, making it perfect for someone passionate about building scalable data infrastructure. You'll be instrumental in shaping our data engineering practices and implementing best-in-class solutions for our analytics and machine learning needs.
Responsibilities For AWS Data Engineer
- Design, develop, and optimize data pipelines using Python, PySpark, and SQL
- Build and manage ETL/ELT workflows for structured and unstructured data
- Leverage AWS services (S3, Glue, EMR, Redshift, Lambda, Athena, Kinesis, Step Functions, RDS) for data engineering solutions
- Implement data lake/data warehouse architectures and ensure data quality, consistency, and security
- Work with large-scale distributed systems for real-time and batch data processing
- Collaborate with data scientists, analysts, and business stakeholders
- Develop and enforce data governance, monitoring, and best practices
- Deploy and manage CI/CD pipelines for data workflows
Requirements For AWS Data Engineer
- Strong programming skills in Python and hands-on experience with PySpark
- Proficiency in SQL for complex queries, transformations, and performance tuning
- Solid experience with AWS cloud ecosystem
- Experience working with data lakes, data warehouses, and distributed systems
- Knowledge of ETL frameworks, workflow orchestration, and automation
- Familiarity with Docker, Kubernetes, or containerized deployments
- Strong understanding of data modeling, partitioning, and optimization techniques
- Excellent problem-solving, debugging, and communication skills
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