对应专业:Computer Science, Statistics, Informatics, Information Systems, or a related field.
所需培训:N
培训时长:
所需培训领域:
前置经验:N
经验时长（月）:
替代领域学历:Y
替代专业:See H.4B.
可替代教育及经验组合:Y
所需教育层级:Bachelor's
其他可替代教育层级:
认可经验年限:8
境外教育背景:Y
可替代职业:Y
可替代职业时长（月）:72
可替代职业名称:Software engineering
是否为正常职业要求:N
是否需要外语:N
特殊技能:H.8A and H.8C In lieu of a Masters degree and six years of experience, employer will accept a Bachelors degree or equivalent in Computer Science, Statistics, Informatics, Information Systems, or a related field and eight years of progressive experience in software engineering.br br H.12 Job requirements are normal for the employer due to the complexity of job duties associated with this position. Employer answered no to Question H.12 based on SVP analysis only.br br Work experience to include 1. Four years of experience in data engineering, data analytics, data warehousing, and machine learning. 2. Four years of experience in a full cycle software engineering. 3. Three years of experience in a full cycle data engineering. 4. Coding with at least 2 of the following Python, Java, PySpark, or Scala. 5. Creating and running Python or Pyspark jobs on AWS Glue or AWS EMR. 6. Building and working with AWS Data Lakes. 7. Working on AWS Data Pipeline and CICD processes. 8. Utilizing big data tools including Hadoop and Spark. 9. Working with AWS cloud services including EMR, RDS, DynamoDB, Athena and Redshift. 10. Working with streamprocessing systems including Kinesis and SparkStreaming. 11. Working with structured, semi structured and unstructured datasets in at least 2 of the following file formats parquet, AVRO, CSV, JSON, ORC, or text. 12. Building and optimizing big data data pipelines, architecture, and data sets. 13. Performing root cause analysis on internal and external data and processes to answer business questions and identify opportunities for improvement. 14. Building processes and supporting data transformation, data structures, metadata, dependency, and workload management. 15. Manipulating, processing, and extracting value from disconnected datasets. 16. Working with message queuing, stream processing, and highly scalable big data data stores. 17. Supporting and working with crossfunctional teams including Product, Data Platform Engineering, and Data Science. Any and all experience can be gained concurrently.br br Hiring Requirements Background Check and Degree Confirmation.