Sr Data Engineer, Video Advertising

Amazon Corporate LLC
Amazon Corporate LLC

Job Overview

Amazon is investing heavily in building a customer centric, world class advertising business across its many unique audio, video, and display surfaces. In this role, you will be on the cutting edge of developing monetization solutions for Live TV, Connected TV and streaming Audio. These are nascent, high growth areas, where advertising monetization is an important, fully integrated part of the core strategy for each business. As a Sr. Data Engineer, you will be tasked with leading data acquisition and processing to building innovative machine learning products to optimize delivery of relevant and performant advertising experiences to customers. As a trusted senior engineering leader you will be given some of the most difficult data management, processing and intelligence generation problems to solve. You will need to develop and/or invent elegant mechanisms and cutting edge infrastructure processing capabilities that drive big data algorithms . As a Sr. Engineer, you will operate as a mix of hands-on builder, and influencer. You will work closely with engineers and scientists contributing to the ongoing development and evolution of the underlying machine learning systems and algorithms. While you will spend the bulk of the time spent as a builder for delivering your own data processing solutions but will be expected to review, influence and support similar development in other areas. As a mentor, you will be a strong partner with science, product and engineering managers, supporting the skill and career development of junior scientists and engineers and educating the broader tech community strategic role of scientific innovations in furthering the advertising business. You’ll be expected to: Design data processing systems and algorithms for optimizing advertising reach, frequency and return on advertising spend Build systems that extract and process volumes of disparate data using a variety of econometric and machine learning approaches. These systems should be designed to scale with exponential growth in data and run continuously. Leverage knowledge of big data technologies, advanced software systems, business intelligence reporting and algorithm development to build our measurement and optimization engines. Serve as a technical lead on our most demanding, cross-functional projects and mentor a team of data engineers and junior machine learning scientists to deliver solutions to internal stakeholders including other modeling teams at Amazon. Contribute intellectual property through patent generation. Functionally decompose complex problems into simple, straight-forward solutions. Understand system inter-dependencies and limitations as well as analytic inter-dependencies to build efficient solutions. Basic Qualifications 5 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 MS/BS in Computer Science, Systems Engineering or Electrical Engineering or Applied Mathematics or equivalent. 5 years of broad software engineering experience including infrastructure and application development Proven track record of leading the delivery of large-scale, high-quality systems Experience using at least one of Python, Java, Scala, Spark Hands-on experience working with R/Matlab/Python or any other statistical language/package is required 5 year experience programming in Java and or C/C++ Knowledge of statistical modeling techniques, e.g., logistic regression, decision trees, SVMs, neural networks Experience with Agile Management, OO Modeling, UNIX, and database related projects 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

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