AWS Data Engineer

Key Responsibilities: Develop and maintain scalable ETL pipelines within the AWS ecosystem. Conduct ETL testing to ensure data integrity, accuracy, and performance. Lead and support large-scale cloud migration projects. Use Python (primary language) along with SQL and PySpark to develop data processing and automation solutions. Orchestrate data workflows using Apache Airflow (including MWAA).Collaborate with cross-functional teams to design data models and implement industry-standard data security and classification methodologies. Monitor, troubleshoot, and optimize pipelines for reliability and performance. Document design, conduct code reviews, and apply test-driven development (TDD) practices. Work in Agile environments, participating in sprint planning and daily stand-ups. Ensure a smooth transition of data during cloud migrations, applying DevOps and CI/CD practices where needed. Qualifications8–12 years of experience in Data Engineering, focusing on ETL, Cloud Migration, and Python development.5+ years of hands-on experience with Python (primary), SQL, and PySpark, applying functional design principles and software design patterns.5+ years building and deploying ETL pipelines across on-prem, hybrid, and cloud environments, with orchestration experience using Airflow.5+ years of production-level AWS experience (MWAA, Glue/EMR (Spark), S3, ECS/EKS, IAM, Lambda).Solid understanding of core statistical principles, data modeling, and data security best practices.5+ years working in Agile development with unit testing, TDD, and design documentation.2+ years of DevOps/CI-CD experience, including Terraform or other Infrastructure-as-Code (IaC) platforms. Excellent problem-solving skills, with the ability to troubleshoot complex data and performance issues. Strong communication skills and ability to work effectively in a hybrid model (3 days onsite in Dallas).

Compensation: $60.00 - $65.00 per hour