Data engineering jobs at Amazon.

AM

Amazon · amazon.jobs · checked today

Data Engineer, Amazon Music, Amazon Music Finance

Skills, with evidence

• Airflow / orchestration
  Construct resilient and scalable data pipelines using SQL/PySpark/Airflow to ingest, process, and transform substantial data volumes from diverse sources into a structured format, ensuring data quality and integrity.must havewrong?
• Data modelling
  Develop data models to support business intelligence, delivering actionable insights and interactive reports to end-users.must havewrong?
• Python
  Construct resilient and scalable data pipelines using SQL/PySpark/Airflow to ingest, process, and transform substantial data volumes from diverse sources into a structured format, ensuring data quality and integrity.must havewrong?
• SQL
  Construct resilient and scalable data pipelines using SQL/PySpark/Airflow to ingest, process, and transform substantial data volumes from diverse sources into a structured format, ensuring data quality and integrity.must havewrong?
• Warehousing
  Devise and implement an efficient, scalable data warehousing solution on AWS, utilizing appropriate NoSQL/SQL storage and database technologies for both structured and unstructured data.must havewrong?
• AWS
  Engage in collaborative efforts with cross-functional teams — data scientists, business intelligence engineers, and Finance Managers — to architect a state-of-the-art data analytics platform on AWS using the AWS Cloud Development Kit (CDK).must have · not practised herewrong?
• Machine learning
  Enable advanced analytics, machine learning, and generative AI capabilities within the platform, extracting predictive and prescriptive insights through tools like EMR and SageMaker.must have · not practised herewrong?
• Spark
  Enable advanced analytics, machine learning, and generative AI capabilities within the platform, extracting predictive and prescriptive insights through tools like EMR and SageMaker.wrong?
• Cost & performance
  Continuously monitor and optimize the performance of data pipelines, databases, and applications, ensuring low-latency data access for analytics and machine learning tasks.wrong?
• Data quality
  Construct resilient and scalable data pipelines using SQL/PySpark/Airflow to ingest, process, and transform substantial data volumes from diverse sources into a structured format, ensuring data quality and integrity.wrong?
• Failure handling
  Construct resilient and scalable data pipelines using SQL/PySpark/Airflow to ingest, process, and transform substantial data volumes from diverse sources into a structured format, ensuring data quality and integrity.wrong?
• Streaming
  - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissionswrong?
• Governance & security
  Implement robust security measures and ensure data compliance with internal requirements, industry standards, and regulations to safeguard sensitive information.not practised herewrong?