Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Common Data Engineer title plus broad AWS/PySpark skills yields moderate competition.
Core AWS, PySpark and ETL skills are highly transferable across industries.
Explicit 8+ years ETL and 4+ years AWS PySpark mandates strict filtering on experience and skills.
Own end-to-end ETL development and deployment on AWS with focus on PySpark scripting and AWS services like S3, Lambda, SNS, Cloud Step Functions.
Operate as an individual contributor responsible for building and maintaining data pipelines, including data lakes and delta table configurations.
Optimize data transformation processes for performance and cost while ensuring data governance, lineage, and metadata management principles are followed.
8+ years of experience in ETL development with at least 4 years using AWS PySpark scripting.
Strong hands-on knowledge of PySpark and Python libraries such as NumPy and Pandas.
Experience deploying AWS-based data solutions utilizing services like S3, Lambda, SNS, and Cloud Step Functions; sound knowledge of AWS services required.
Work Experience Required: 8+ years in ETL development with AWS focus.
Experienced individual contributor comfortable with end-to-end AWS data integration frameworks and orchestration (e.g., MWAA/Airflow).
Skilled in managing large-scale data transformations involving semi-structured and structured data, with an understanding of data lakes and delta table architectures.
Proactive in computing and cost optimization strategies within cloud data environments, with knowledge of metadata management and data governance frameworks.