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Mid-level, popular data-engineer role with broad AWS/Kafka/Python requirements increases applicant competition.
Core data engineering skills (Python, SQL, AWS, Kafka) are highly transferable across industries.
Explicit 2–4 year requirement plus specific AWS, Kafka, Python and SQL skills enforce moderate screening.
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Design, build, and maintain scalable real-time and batch data pipelines handling large volumes of data.
Manage scalable data architecture and data lakes on AWS services like S3, Redshift, and Glue to enable accessible data for analytics and data science teams.
Collaborate cross-functionally with Product, Backend Engineering, and Data Science teams to deliver features impacting business metrics and user experience.
2–4 years of hands-on experience coding in Python, with advanced SQL skills; Java or Go experience is a plus.
Experience with AWS data services including Redshift, S3, Glue, CloudFormation, or ECS.
Hands-on experience or strong understanding of Kafka or AWS Kinesis for real-time streaming data.
Work Experience Required: 2–4 years in data engineering or relevant roles.
Experienced in building and optimizing high-volume, complex ETL/ELT pipelines within microservices architectures.
Proficient in cloud-based big data infrastructure, specifically AWS ecosystem tools for data lakes and warehousing.
Skilled in working across teams and domains (product, backend, data science) to translate data engineering solutions into measurable business impact.