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Data Engineer II, DSP Analytics

Amazon.com
IND
Full-time
Onsite
Discovered 1 weeks ago
Data engineeringData modelingData warehousingETL pipelinesPythonRuby
Free

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Data engineeringData modelingData warehousing
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About the Role

The DSP Analytics team is seeking a Data Engineer to develop real-time and batch pipelines that generate actionable insights.

The role supports Amazon's Delivery Service Partner program through data infrastructure, analytics, and Generative AI solutions.

The team works with Data Scientists, Economists, and Analysts to turn research into scalable products.

Key Job Responsibilities

  • Lead and design complex data architectures for scalability, security, and business alignment.
  • Drive implementation of large-scale data pipelines and ETL processes using AWS technologies.
  • Identify and resolve system-wide data challenges, including performance bottlenecks and architectural deficiencies.
  • Establish data engineering best practices for governance, security, and operational excellence.
  • Ensure data solutions are auditable, accessible, and maintainable across the organization.
  • Collaborate with cross-functional teams to deliver innovative data solutions.

Basic Qualifications

  • At least 3 years of data engineering experience.
  • Experience with data modeling, data warehousing, and building ETL pipelines.
  • Bachelor's degree in Computer Science, Engineering, or a related field.
  • Programming experience in at least one modern language such as Python, Ruby, Golang, Java, C++, C#, or Rust.
  • At least 2 years of analyzing and interpreting data using Redshift, Oracle, NoSQL, or similar technologies.

Preferred Qualifications

  • Experience with AWS technologies such as Redshift, S3, Glue, EMR, Kinesis, Firehose, Lambda, and IAM roles and permissions.
  • Experience with non-relational databases or data stores, including object, document, key-value, graph, or column-family stores.

About the Team

The Amazon DSP Analytics team supports business pillars across the DSP organization and enables data-, insights-, and science-driven decision-making.

The team works on complex business and data problems, large-scale technical solutions, and operational excellence.

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