Hundreds of millions of customers, billions of transactions, petabytes of data… How to use the world’s richest collection of e-commerce data to provide superior value and better paying experience to customers ? The Amazon Payments Team manages all Amazon branded payment offerings globally. These offerings are growing rapidly and we are continuously adding new market-leading features and launching new products. Amazon.com has a culture of data-driven decision-making and demands business intelligence that is timely, accurate, and actionable. This team provides a fast-paced environment where every day brings new challenges and new opportunities.
Our team of high caliber software developers, data scientists, statisticians and product managers use rigorous quantitative approaches to ensure that we target the right product to the right customer at the right moment, managing tradeoffs between click through rate, approval rates and lifetime value. In order to accomplish this we leverage the wealth of Amazon’s information to build a wide range of probabilistic models, set up experiments that ensure that we are thriving to reach global optimums and leverage Amazon’s technological infrastructure to display the right offerings in real time.
As a Data Engineer you will be working in one of the world’s largest and most complex data warehouse environments. You should be passionate about working with huge data sets and be someone who loves to bring datasets together to answer business questions. You should have deep expertise in creation and management of datasets. You will build data analytical solutions that will address increasingly complex business questions.
You should be expert at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports. These solutions will be fault tolerant, self-healing and adaptive. You will be working on developing solutions that provide some of the unique challenges of space, size and speed. You will implement data analytics using cutting edge analytics patterns and technologies that are inclusive of but not limited to various AWS Offerings – EMR, Lambda, Kinesis, and Spectrum. You will extract huge volumes of structured and unstructured data from various sources (Relational /Non-relational/No-SQL database) and message streams and construct complex analyses. You will write scalable code and tune performance running over billion of rows of data. You will implement data flow solutions that process data on Spark, Redshift and store in both Redshift and File based storage (S3) for reporting and adhoc analysis.
You should be detail-oriented and must have an aptitude for solving unstructured problems. You should work in a self-directed environment, own tasks and drive them to completion.
You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions and to build data sets that answer those questions. You own customer relationship about data and execute tasks that are manifestations of such ownership, like ensuring high data availability, low latency, documenting data details and transformations and handling user notifications and training.
· Bachelor’s degree in Computer Science, Mathematics, Statistics, Finance or related technical field.
· 3+ 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
· Experience with data modeling, data warehousing, and building ETL pipelines
· Hands-on experience and advanced knowledge of SQL
· Experience using big data technologies (Hadoop, Hive, Hbase, Spark, EMR, etc.)
· Experience with coding languages like Python/Java/Scala
· Experience in working with business customers to drive requirements analysis
· Exposure to large databases, BI applications, data quality and performance tuning
· Excellent written and spoken communication skills
· Experience partnering with business owners directly to understand their requirements and provide data which can help them observe patterns and spot anomalies
· Experience with web technology to develop dashboards
· Practical Knowledge of Linux or Unix shell scripting
· Experience in processing large volume of data
· Experience providing technical leadership and mentoring other engineers for best practices on data engineering
· Knowledge of software engineering best practices across the development life cycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
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