ETIC, AWS Data Engineer, Senior Associate

Cairo - Eticsenior

OtherAws Data Engineer

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Quick Summary

Key Responsibilities

AWS Data Platform: AWS Glue, Amazon Redshift, Amazon S3, AWS Lambda, Amazon Kinesis, and AWS Step Functions. Proficiency in Python and SQL for large-scale data processing and transformation.

Technical Tools

OtherAws Data Engineer

Advisory

Technology

Advisory - Other

Senior Associate

As a Senior Associate in the Data Engineering team, you will play a key role in designing, building, and optimizing modern data platforms and pipelines on AWS. You will work within cross-functional teams to deliver scalable, secure, and high-performing data solutions that enable advanced analytics, AI, and business insights for enterprise clients.
This role requires a strong understanding of cloud data architecture and hands-on experience with AWS data services for batch and real-time data engineering.

Responsibilities

~1 min read

→Design, develop, and maintain end-to-end data pipelines across structured, semi-structured, and unstructured data sources.
→Implement data ingestion, transformation, and orchestration frameworks leveraging AWS Glue, Amazon Redshift, AWS Step Functions, and Amazon Managed Workflows for Apache Airflow (MWAA).
→Develop and optimize ETL/ELT processes using PySpark, SQL, and Delta Lake to ensure high performance and scalability.
→Implement and enforce data quality, lineage, and governance practices.
→Work closely with solution architects to design modern data architectures and ensure compliance with security and privacy standards.
→Participate in client workshops and technical discussions to translate business needs into technical designs.

Requirements

~1 min read

3–6 years of experience in data engineering, preferably in a consulting or enterprise environment.
Strong hands-on experience with:
- AWS Data Platform: AWS Glue, Amazon Redshift, Amazon S3, AWS Lambda, Amazon Kinesis, and AWS Step Functions.
Proficiency in Python and SQL for large-scale data processing and transformation.
Solid understanding of data modeling, medallion architecture, and lakehouse principles.
Familiarity with CI/CD pipelines, DevOps, and version control (e.g., Git, AWS CodePipeline).
Knowledge of data governance, lineage, and observability tools.
Experience with performance optimization, cost control, and best practices in cloud environments.

Not Specified

Nice to Have

~1 min read

Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, Communication, Creativity, Data Anonymization, Data Architecture, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Databricks Unified Data Analytics Platform, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling, Data Pipeline {+ 27 more}