As an AWS Data Engineer, you will design, develop, and maintain robust data pipelines within the AWS ecosystem. Your primary focus is transforming raw data into actionable insights by leveraging cloud-native ETL tools and high-performance warehousing. You’ll bridge the gap between raw data sources and downstream analytics, ensuring data quality, scalability, and efficiency.

Pipeline Development & ETL Build & Orchestrate: Design end-to-end ETL/ELT pipelines using AWS Glue and Lambda to automate data movement Big Data Processing: Write and optimize complex data transformation logic using Python and PySpark. Modern Warehousing: Manage and optimize data storage and querying within Amazon Redshift (and potentially Snowflake). Data Architecture & Management Storage Strategy: Organize and maintain scalable "Data Lakes" using Amazon S3, ensuring proper partitioning and security. Advanced Querying: Execute and tune complex SQL queries for data validation, modeling, and analysis. Modeling: (Preferred) Implement modular data modeling and version control using dbt. Optimization & Maintenance Performance Tuning: Monitor and refine Spark jobs and SQL queries to reduce latency and cloud costs. Data Integrity: Ensure high data quality through automated testing and rigorous validation checks.