Data Engineer
Seattle, Washington, USA
Amazon.com
Free shipping on millions of items. Get the best of Shopping and Entertainment with Prime. Enjoy low prices and great deals on the largest selection of everyday essentials and other products, including fashion, home, beauty, electronics, Alexa...Consumer Engagement Finance BI team (CEFBI) supports Worldwide Consumer Engagement finance and Consumer Engagement teams and a broad set of category stakeholders across Consumer business. CEFBI acts as the backbone between the Business and Finance by providing data for business reviews and decision-making while also enabling finance team to exercise high degree of controllership within complex datasets. The team owns the data engineering infrastructure for one of the largest data marts at Amazon (1,000+ terabytes in data size, 80+ in Redshift nodes, 6000+ in weekly query count and 200+ in downstream data teams consuming from the platform) and specializes in transit and click level data that enable marketing teams around the world to manage and optimize their business.
Core Responsibilities:
1) Leading the design of scalable big data platform/solutions used to ingest and process Amazon-scale traffic data.
2) Building end-to-end solutions that enable business stakeholders, BI engineers and analysts to self-serve their business intelligence needs.
3) Coaching fellow data engineers and ultimately raising the bar of data engineering excellence across Amazon’s Consumer Marketing organizations.
The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics. He/she is an expert with data modeling, ETL design and business intelligence tools and passionately partners with the business to identify strategic opportunities where improvements in data infrastructure creates out-sized business impact. He/she is a self-starter, comfortable with ambiguity, able to think big (while paying careful attention to detail), and enjoys working in a fast-paced and global team. It's a big ask, and we're excited to talk to those up to the challenge!
Basic Qualifications
· 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 or higher in a quantitative/technical field (e.g. Computer Science, Economics, Finance, Mathematics, Statistics, Engineering).
· Relevant experience in one of the following areas: data science, data engineering, business intelligence or business analytics.
· Expert-level proficiency in writing complex, highly-optimized SQL queries across large datasets
· Proven track record of independently delivering big data solutions.
· Ability to manage competing priorities simultaneously and drive projects to completion.
Preferred Qualifications
· 7+ years of experience as a data engineer, BI Engineer, Business/Financial Analyst or Systems Analyst in an internet-based company with large, complex data sources.· Superior dashboard building capabilities (Tableau or MicroStrategy).
· Experience with AWS services including S3, Redshift, EMR and RDS. · Experience with Machine Learning concepts and tools (Python experience is a strong plus).
· Knowledge with statistical and/or econometric modeling.
· Experience in BI/DW as a change leader providing strategic research, recommendations, and implementations.
· Demonstrated experience managing and prioritizing a project backlog.
· Demonstrated experience defining and driving an analytics roadmap.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Tags: AWS Big Data Business Analytics Business Intelligence Computer Science Data Warehousing Economics Engineering ETL Finance Machine Learning Mathematics Pipelines Python Redshift Research SQL Statistics Tableau
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