Snr Data Engineer, Prime

1 month ago


Chicago, Illinois, United States Amazon Full time
About the Role

We are seeking a highly skilled Data Engineer to join our Prime Science team at Amazon. As a Data Engineer, you will play a critical role in building and maintaining our data infrastructure, ensuring the seamless flow of data across our systems.

Key Responsibilities:

  • Design and implement data pipelines for machine learning applications at Amazon scale
  • Advance the state of data engineering infrastructure on the latest tools (AWS Glue, Spark, AWS EMR, EMR Serverless, Ray etc...)
  • Improve and raise the bar on proactive data quality monitoring and alarming (e.g., anomaly detection) across big data pipelines

About the Team

Our team has a vision to enable Prime Science to be adopted WW across Amazon so as to drive the best possible customer experience. We set and raise engineering excellence standards in Amazon for science-based products.

We are a unique team as we sit at the intersection of building engineering systems at Amazon scale and availability, incorporating the latest science (econometrics, ML, AI etc...), and delivering cross-cutting applications to delight over 200 million customers.

Building and our science products at-scale and in real-time, creates interesting new data engineering challenges. These give a person in this role a head start in solving artificial intelligence challenges that will be ubiquitous 3-5 years from now.

Requirements

  • 5+ years of data engineering experience
  • Experience with data modeling, warehousing and building ETL pipelines
  • Experience with SQL
  • Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
  • Experience mentoring team members on best practices

Preferred Qualifications

  • Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
  • Experience operating large data warehouses
  • Knowledge of distributed systems as it pertains to data storage and computing
  • Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence


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