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AWS Data Engineer

Negotiable Salary

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Ohio, USA

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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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Ohio, USA
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