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Specialized senior data engineer with AWS/Spark skills at a lesser-known startup yields moderate applicant competition.
AWS, Spark, and data architecture skills are transferable across industries but require domain-specific experience.
Explicit 7–12 years plus mandatory AWS, Spark, and data architecture requirements create high shortlisting strictness.
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Design, develop, and optimize ETL/ELT data pipelines using AWS data engineering services.
Manage and tune big data workflows leveraging Apache Spark, ensuring performance optimization and troubleshooting.
Oversee data architecture components including Data Lakes, Data Warehousing, and Data Modeling within AWS ecosystem.
7-12 years of experience in Data Engineering.
Strong hands-on experience with AWS services: S3, Glue, Redshift, EMR, Lambda, IAM, and CloudWatch.
Proficient in advanced Python, PySpark, and SQL programming.
Notice period up to 15 days is acceptable.
Experienced in working with Agile/Scrum development methodologies.
Demonstrates strong problem-solving skills with ability to handle complex production-level issues.
Familiar with version control using Git and performance tuning of data pipelines in a cloud environment.