Role: AWS Engineer Location: Austin, TX Duration: 6+ months (Multiple Roles, multiple levels) Summary: We are seeking a highly skilled AWS Engineer to join a cutting-edge data platform. The ideal candidate will have deep experience in AWS infrastructure, data lake architecture, and large-scale data pipeline development. This role demands hands-on expertise in AWS services such as Glue, EMR, Redshift, S3, and SageMaker, along with strong SQL, Python, and PySpark skills. Key Responsibilities: • Architect, develop, and maintain scalable AWS-based data lake and ETL/ELT solutions. • Leverage AWS Glue, EMR, CloudFormation, Development EndPoints, S3, Redshift, and EC2 to build distributed and secure data platforms. • Set up and optimize Jupyter/SageMaker Notebooks for advanced analytics and data science collaboration. • Develop robust data pipelines using Spark clusters, ensuring performance, fault-tolerance, and maintainability. • Build connectors to ingest and process data from distributed sources using various integration tools and frameworks. • Write efficient, production-grade SQL, Python, and PySpark code for data transformation and analysis. • Lead proof-of-concept (PoC) efforts and scale them into production-ready systems. • Stay current with emerging data and cloud technologies, offering guidance on how to apply them effectively to solve complex technical and business challenges. • Collaborate with cross-functional teams, including data scientists, analysts, and product stakeholders. Required Skills: • Proven experience setting up and managing AWS infrastructure with CloudFormation, Glue, EMR, Redshift, S3, EC2, and SageMaker. • Strong knowledge of Data Lake architecture and data ingestion frameworks. • 5+ years of experience in Data Engineering and Data Warehouse development. • Advanced proficiency in SQL, Python, and PySpark. • Experience designing and optimizing complex Spark-based data pipelines on AWS. • Ability to troubleshoot performance bottlenecks and production issues in large-scale distributed systems. • Strong leadership in taking PoCs to production through structured engineering practices. Preferred Qualifications: • AWS certifications (e.g., AWS Certified Data Analytics – Specialty, Solutions Architect). • Prior experience at an enterprise-scale client such as Amazon or other FAANG companies. • Familiarity with DevOps practices and tools like Terraform, Jenkins, Docker, and Git.