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Mid-level, common Data Engineer skills (AWS/Spark/Python) plus flexible/remote options increase applicant competition.
Data engineering skills (ETL, Spark, AWS, SQL) are highly transferable across industries.
Explicit 4–6 years requirement plus mandatory AWS, Spark, Python, and SQL skills make filters stringent.
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Design, build, test, and maintain scalable ETL/ELT data pipelines using Python, Spark, and Pyspark.
Architect and optimize big data processing systems using Apache Spark and Hadoop within AWS cloud environment.
Build and manage cloud-native data architectures on AWS services including S3, EMR, Glue, Redshift, Lambda, and Athena, ensuring data quality and governance.
4 to 6 years of professional experience in data engineering or software development.
Hands-on experience with AWS cloud platform, specifically building data lakes and data warehouses.
Proficiency in Apache Spark and distributed computing frameworks; experience with Kafka or Kinesis is required.
Strong programming skills in Python and advanced SQL expertise.
Experienced in designing dimensional data models and optimizing query performance for analytics at scale.
Capable of implementing automated data quality checks and adhering to data governance and security standards in cloud environments.
Familiarity with Infrastructure as Code (Terraform or CloudFormation) and orchestration tools (Apache Airflow or Step Functions) is a plus but not mandatory.