- Seattle, WA
Are you interested in building high-performance and globally scalable reporting and analytics infrastructure that support Amazon's Global Real Estate and Facilities (GREF) organization's current and future growth? Are you seeking an environment where you can drive innovation leveraging the scalability and innovation with Amazon's AWS cloud services? Do you have a passion for ensuring a positive customer experience? This is the opportunity for you.
In Finance Automation, we built technology to simplify and automate the processes Amazon uses to manage its financial relationships with external parties. We are on a journey to create technology that simplifies the processes that Amazon uses to procure, collect and pay. We recently formed a new GREF Technology team which is the software development team for GREF. This team builds products and tools to enable Amazon's corporate real estate team as they build and operate the company's facilities worldwide.
The GREF Tech team is looking for a passionate, solution-oriented Data Engineer to lead the implementation of the analytical data infrastructure that will guide the decision making behind initiatives such as space planning, design and construction, corporate security, travel, transportation, lease, facilities, and other key projects within the Global Real Estate and Facilities (GREF) domain.
The team is committed to building the next generation reporting and analytics platform to support Amazon's rapidly growing workforce and improve employee experience. Our projects span multiple organizations and require coordination of data integrity, test design, analysis, validation, and documentation.
• You will act as the business-facing subject matter expert for data storage and feature instrumentation, with the responsibility of managing end-to-end execution and delivery across various work streams.
• You will help drive data architecture across many large datasets, perform exploratory data analysis, implement new data pipelines that feed into or from critical data systems at Amazon.
• You will be responsible for designing and implementing scalable ETL processes in the AWS platform to support the rapidly growing and dynamic business demand for data, and use it to deliver the data as service which will have an immediate influence on day-to-day decision making and strategic initiatives.
• You will hold a highly visible role that requires interaction with leaders across Finance Automation and GREF.
• 3+ years of experience as a Data Engineer or in a similar role
• Experience with data modeling, data warehousing, and building ETL pipelines
• Experience in SQL
• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical discipline
• 5+ years of industry experience in Software Development, Data Engineering, Business Intelligence, Data Science, or related field with a track record of manipulating, processing, and extracting value from large datasets
• Hands-on experience and advanced knowledge of SQL
• Experience in Data Modeling, ETL Development, and Data Warehousing
• Experience using business intelligence reporting tools such as Tableau, Quick Sight, or Cognos
• Experience using big data technologies (Hadoop, Hive, Hbase, Spark, EMR, etc.)
• Knowledge of Data Management fundamentals and Data Storage principles
• Proficiency in at least one modern programming language such as Python, Perl, Ruby, or Java.
• Basic knowledge of UNIX shell scripting
• Experience working with AWS big data technologies (Redshift, S3, EMR)
• Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
• Proven success in communicating with users, other technical teams, and senior management to collect requirements, describe data modeling decisions and data engineering strategy
• Experience providing technical leadership and educating other engineers for best practices on data engineering
• Familiarity with statistical models and data mining algorithms.
• Masters in computer science, mathematics, statistics, economics, or other quantitative fields
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