Aws Data Engineer (Pyspark)

- Data Pipeline Development: Design, implement, and manage scalable ETL/ELT pipelines using AWS services and Databricks.
- Data Integration: Ingest and process structured, semi-structured, and unstructured data from multiple sources into AWS Data Lake or Databricks.
- Data Transformation: Develop advanced data processing workflows using PySpark, Databricks SQL, or Scala to enable analytics and reporting.
- Databricks Management: Configure and optimize Databricks clusters, notebooks, and jobs for performance and cost efficiency.
- AWS Architecture: Design and implement solutions leveraging AWS-native services like S3, Glue, Redshift, EMR, Lambda, Kinesis, and Athena.
- Collaboration: Work closely with Data Analysts, Data Scientists, and other Engineers to understand business requirements and deliver data-driven solutions.
- Performance Tuning: Optimize data pipelines, storage, and queries for performance, scalability, and reliability.
- Monitoring and Security: Ensure data pipelines are secure, robust, and monitored using CloudWatch, Datadog, or equivalent tools.
- Documentation: Maintain clear and concise documentation for data pipelines, workflows, and architecture