Offers “Amazon”

Expires soon Amazon

Data Engineer II - AMZ3542

  • Seattle (King)
  • IT development

Job description



DESCRIPTION

MULTIPLE POSITIONS AVAILABLE

Entity: Amazon.com Services, Inc., an Amazon.com Company

Title: Data Engineer II

Worksite: Seattle, WA

Position Responsibilities:

Design, develop, implement, test, document, and operate large-scale, high-volume, high-performance data structures and data workflows for business intelligence analytics. Implement data structures and data workflows using best practices in data modeling, ETL/ELT processes, SQL, Bigdata, and AWS technologies. Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions that work well within the overall data architecture. Analyze source data systems, resolve ambiguity and drive best practices in source teams. Participate in the full development life cycle, end-to-end, from design, implementation and testing, to documentation, delivery, support, and maintenance. Produce comprehensive, usable dataset documentation and metadata. Evaluate and make decisions around dataset implementations designed and proposed by peer data engineers. Evaluate and make decisions around the use of new or existing software products and tools. Mentor junior data engineers.

Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000

Desired profile



BASIC QUALIFICATIONS

Position Requirements:

Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field and four years of experience in the job offered, or as a Data Engineer, Database Developer, or a related occupation. Must have four years of experience in the following skill(s): writing high-quality, maintainable, performant and robust code in SQL; writing code in Python or equivalent programming language; experience with design, optimization and performance tuning of ETL workloads; hands-on experience in data modeling and deep understanding of trade-offs; and experience with distributed systems, such as MapReduce, MPP architectures, or NoSQL databases.

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