AWS Data Engineer
Job Summary
We are looking for an experienced AWS Data Engineer with strong expertise in Python and PySpark to design, build, and maintain large-scale data pipelines and cloud-based data platforms. The ideal candidate will have hands-on experience with AWS services , distributed data processing, and implementing scalable solutions for analytics and machine learning use cases.
Key Responsibilities
· 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 to deliver high-quality, reliable data solutions.
· Develop and enforce data governance, monitoring, and best practices for performance optimization.
· Deploy and manage CI/CD pipelines for data workflows using AWS tools (CodePipeline, CodeBuild) or GitHub Actions.
Required Skills & Qualifications
· 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 (S3, Glue, EMR, Redshift, Athena, Lambda, etc.).
· Experience working with data lakes, data warehouses, and distributed systems .
· Knowledge of ETL frameworks , workflow orchestration (Airflow, Step Functions, or similar), 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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