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High due to a popular Data Engineer title, metro location, and broad AWS/big-data skill requirements.
Medium — core data engineering skills are transferable, but healthcare/regulatory domain knowledge is beneficial.
High due to an explicit 10-year minimum combined with specific AWS and big-data tool expectations.
Build and optimize ETL pipelines for data extraction, transformation, and loading using SQL and AWS technologies to ensure data accuracy, stability, and availability.
Develop and maintain big data infrastructure leveraging AWS Redshift, Hadoop, and tools like Airflow, AWS Glue, S3, EMR, Kafka/Kinesis.
Provide data analysis support by extracting detailed insights, resolving data engineering issues, and ensuring compliance with validation and regulatory standards.
Bachelor's or Master's degree in Computer Science, Information Management, Data Science, Econometrics, AI, Applied Mathematics, Statistics, or equivalent.
Minimum 10 years experience required with a Bachelor's degree in relevant areas like Data Handling, Analytics, or AI Modeling; no prior experience required with a Master's degree.
Strong knowledge of big data tools such as AWS Redshift, Hadoop, map reduce.
Work location requires working in-office at least 3 days per week (hybrid model).
Experienced in managing large-scale ETL pipelines and optimizing data workflows for performance and accuracy.
Familiar with a broad range of AWS data services and streaming data technologies like Kafka/Kinesis.
Capable of ensuring data quality and compliance within a regulated environment while supporting diverse business units with actionable data insights.