AWS Data Engineer, Streaming & ETL
Overview
Design and build AWS-based batch and streaming data pipelines for enterprise-scale systems. Collaborate with data architects and analysts to deliver real-time insights. Own the data quality framework from day one. This role drives mission-critical data reliability across hybrid deployments.
What You'll Do7
- 1Build AWS data pipelines using Kinesis, Lambda, and Glue within the first 90 days.
- 2Design streaming solutions with Kafka and Flink for real-time processing.
- 3Implement data validation and monitoring using Great Expectations and CloudWatch.
- 4Optimize Spark jobs for batch and streaming workloads.
- 5Lead migration of on-prem ETL processes to AWS S3 and Redshift.
- 6Drive data quality initiatives with automated testing and alerting.
- 7Collaborate with teams to ship scalable data models in Snowflake or BigQuery.
Requirements7
- 110-15 years in data engineering with AWS focus.
- 25+ years building streaming pipelines with Kafka and Kinesis.
- 35+ years writing production Python and SQL.
- 43+ years with Spark and Flink for real-time analytics.
- 5Hands-on AWS services: S3, Redshift, Glue, Lambda.
- 6Experience with data quality tools like Great Expectations or Deequ.
- 7Proven record of delivering enterprise-grade data solutions.
Salary Insight
Salary not disclosed in listing
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