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Metro-based, generalist Data Engineer role with broad AWS/Hadoop skillset increases applicant competition.
Requires specialized big-data AWS/Hadoop and Spark expertise, reducing cross-industry transferability.
Explicit 7+ years plus mandatory AWS/Hadoop and production data engineering skills makes shortlisting strict.
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Build and optimize scalable batch and streaming data pipelines using AWS services and Hadoop ecosystem tools.
Design and manage AWS-based data lakes and warehouses with focus on cost, performance, reliability, and security.
Collaborate with analytics, machine learning, and BI teams to deliver curated, governed datasets while participating in code reviews and documentation.
7+ years of experience in Data Engineering or related roles involving large-scale distributed data systems.
Strong hands-on experience with AWS data services (S3, Glue, EMR, Athena, Lambda, Redshift, IAM, CloudWatch) and Hadoop ecosystem tools (HDFS, Hive, Spark, Kafka, Oozie/Airflow).
Proficiency in SQL, Python and/or Scala, Shell scripting, and familiarity with CI/CD pipelines using Git, Docker, Jenkins or GitHub Actions.
Bachelor’s degree in Computer Science, Engineering, Information Systems, Mathematics or related field or equivalent practical experience.
Experienced in designing and optimizing both batch and streaming data pipelines at scale using Spark, Kafka/Kinesis and AWS ecosystem.
Strong practical knowledge of data modeling, partitioning, metadata management, data quality, security and governance in cloud data platforms.
Proficient in collaboration with cross-functional teams for delivering reliable, secure, and cost-efficient data solutions in production environments.