AWS Data Engineer

Job Title: Data Engineer - AWS & PySpark

Experience Required: 5-8 years

Job Summary:

We are seeking a skilled Data Engineer with 5-8 years of experience to design, implement, and maintain robust, scalable data architectures on AWS. The ideal candidate will be highly proficient in PySpark and experienced with AWS cloud data services including S3, Glue, and Redshift. You will work closely with cross-functional teams to enable seamless data flows and ensure efficient ETL pipelines across our cloud infrastructure.

Key Responsibilities:

• Design, implement, and maintain scalable and robust data architectures on AWS.
• Utilize AWS services such as S3, Glue, and Redshift for data storage, processing, and analytics.
• Develop and implement ETL processes using PySpark to extract, transform, and load data into AWS storage solutions.
• Collaborate with cross-functional teams to ensure seamless data flow across different AWS services.
• Ensure high-quality code and adherence to best practices in data engineering and cloud deployment

  .

Must-Have Skills:

• 5-8 years of experience in Data Engineering.
• Strong hands-on experience with PySpark for ETL and data processing tasks.
• Proven experience working with AWS data services: S3, Glue, and Redshift.
• Solid understanding of data modeling, SQL, and database design.
• Experience with designing and building data pipelines on AWS.

Good-to-Have Skills:

• Knowledge of Airflow, Terraform, or other orchestration/IaC tools.
• Familiarity with DevOps practices in cloud-based data environments.
• Experience with real-time data processing (e.g., Kinesis, Kafka).
• Exposure to data governance, security, and compliance frameworks.
• Working knowledge of other AWS services like Lambda, Athena, or EMR.