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Mid-level, popular data-engineer role at a recognizable services firm increases applicant competition.
Core data engineering skills on AWS and PySpark are broadly transferable across industries.
Explicit 5–10 year requirement plus mandatory AWS, PySpark, and Glue skills raise selection strictness.
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Design, implement, and maintain scalable data pipelines on AWS using PySpark, AWS Glue, and other services.
Develop and optimize ETL processes for data extraction, transformation, and loading using PySpark.
Ensure efficient data flow, storage, and processing to support organizational data needs.
5 to 10 years of experience as a Data Engineer with AWS data solutions focus.
Proficient in Python and advanced PySpark programming skills for data processing.
Hands-on experience with AWS Glue or other ETL tools.
Work Experience Required: 5 to 10 years in relevant data engineering roles.
Experienced in building end-to-end data solutions leveraging AWS services such as S3, Glue, and Athena.
Strong ability to optimize and automate data pipelines using PySpark on large-scale data sets.
Capable of independently managing robust data engineering tasks within AWS cloud environments.