AWS Data

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-Off

SQL - Expert (Must have)

AWS (Redshift / Lambda / Glue / SQS / SNS / Cloudwatch / Step function / CDK)) - Expert (Must have)

Pyspark -Expert

Python - Intermediate (Must have or Pyspark knowledge)

Strong Comm Skills

Code & Architecture Review- Ensuring quality and scalability of code / data pipelines

Team Leadership Skills - Leading sprint planning, retrospectives and Assigning and reviewing tasks

Project Management Skills - Agile / Scrum methodology, Risk & deadline management

Mentoring & Coaching - Upskilling team on AWS best practices and data architecture

Stakeholder Communication - Translating tech into business value. Reporting to non-technical managers

Exposure 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 Quality

Involved in Code Review & Sign-Off

AWS Airflow - Intermediate (Nice of have)

ETL Process

Skills Required

Pyspark, AWS Glue, Cloudwatch, CDK, Sql, Amazon Redshift, Sqs, Sns, Databricks, Python