Data Engineer

- 3+ years of data engineering experience
- Experience with SQL
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with Redshift, Oracle, NoSQL etc.
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
- Exposure to large databases, BI applications, data quality and performance tuning
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Experience with distributed systems as it pertains to data storage and computing
- Experience partnering with business owners directly to understand their requirements and provide data which can help them observe patterns and spot anomalies.
- Practical Knowledge of Linux or Unix shell scripting.
- 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.

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.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $118,900/year in our lowest geographic market up to $205,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
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.