Data Engineer, Workforce Solutions - Analytics and Tech

- 1+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.

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 $91,200/year in our lowest geographic market up to $185,000/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.
At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. If you'd like to help us build the place where people can find and buy anything online, this is your chance to make history. Come help us create innovative capabilities for Amazon's Associates, managers, and leaders to support the evolution of People eXperience and Technology (PXT).
Our solutions give Amazon associates around the globe the flexibility to enrich their job experience through pay incentives and job mobility. We thrive on developing world-class experiences for associates worldwide to empower them to increase their income, explore job options, train, choose, and progress in their careers at Amazon. As a member of this team, you are changing people's lives by creating the tools that help them advance in their careers!
The Workforce Solutions team builds and delivers world-class products/services for Amazon’s hourly associates. We are using modern technologies to solve complex human problems at scale. We deliver an engaging, intuitive, personalized experiences that empower employees to quickly take control of their work life anytime, anywhere.

Do you love building data solutions at scale? Are you excited by the opportunity to design tools and infrastructure needed to analyze large volumes of data? Do you want to help solve big data problems, and partner with stakeholders to understand how to best design and implement data solutions that provide answers to key business questions? Do you want to be a part of a fast-paced environment and contribute to one of the most visited sites on the Internet?
At Amazon, we hire the best minds in technology to innovate on behalf of our customers. The focus we have on our customers is why we are one of the world’s most beloved brands – customer obsession is part of our company DNA. Data engineers use technology to solve complex problems and get to see the impact of their work first-hand.
The challenges data engineers solve for at Amazon are big and impact millions of customers, sellers, and products around the world. Our path is not always simple, so we are selective about who joins us on this journey. There is a certain kind of person who takes on this role at Amazon – someone who is excited by the idea of creating new products, features, and services from scratch while managing ambiguity and the pace of a company whose ship cycles are measured in weeks, not years.
We’re on the lookout for the curious, those who think big and want to define the world of tomorrow.
At Amazon, you will grow into the high impact, person you know you’re ready to be. Every day will be filled with exciting new challenges, developing new skills, and achieving personal growth.
How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow.


Key job responsibilities
• Design, develop, implement, test, document, and operate large-scale, high-performant data structures for big data analytics.
• Translate business requirements into robust, scalable, operable solutions that work well within the overall data architecture.
• Solving complex problems by aggregating multiple large datasets to enable informed decision making.
• Working with large datasets and developing big data pipelines that move data from source systems to data warehouses, S3 data lakes, and other data storage and processing systems using Big Data and Cloud technologies.
• Implementing data structures using best practices in data modeling, ELT/ETL (Extract/Transform/Load) processes, using big data tools such as Spark, Hive, SQL, Apache Airflow, AWS Glue, EMR, Lambda, S3, Redshift and OLAP technologies.
• Following software engineering best practices in writing code (using programming languages like Python, SQL and Scala), reviewing code with peers and testing for all related data processing systems.
• Maintaining and optimizing infrastructure required to support scalable and high availability data lakes and data pipelines to store and process petabyte scale datasets.