Data Engineer

AWS Data Engineer

Design and build scalable data pipelines using AWS services like AWS Glue, Amazon Redshift, and QS/SNS/Cloudwatch/Step function/CDK.Develop efficient ETL processes for data extraction, transformation, and loading into data warehouses and lakes.Create and manage applications using Python, Pyspark, SQL, Databricks, and various AWS technologies.Automate repetitive tasks and build reusable frameworks to improve efficiency.Involved in Arch Design and Review, Team Leadership Skills, Management Skills, Involved in Code Review & Sign-OffSQL - Expert (Must have)AWS (Redshift/Lambda/Glue/SQS/SNS/Cloudwatch/Step function/CDK)) - Expert (Must have)Pyspark -ExpertPython - Intermediate (Must have or Pyspark knowledge)Strong Comm SkillsCode & Architecture Review- Ensuring quality and scalability of code/data pipelinesTeam Leadership Skills - Leading sprint planning, retrospectives and Assigning and reviewing tasksProject Management Skills - Agile/Scrum methodology, Risk & deadline managementMentoring & Coaching - Upskilling team on AWS best practices and data architectureStakeholder Communication - Translating tech into business value. Reporting to non-technical managersExposure to cost optimization strategies in AWS, Data Lake Vs. Data Warehouse Architecture, Designing Scalable ETL Pipelines, Real-Time Data Ingestion, Data Partitioning and Performance Optimization, Security and Compliance, Data QualityInvolved in Code Review & Sign-OffAWS Airflow - Intermediate (Nice of have)ETL Process