Pay Growth
?
out of 5
Not enough data
Not enough info to score pay or growth
Data Engineer, Workforce Solutions
Job
Amazon.com, Inc.
Bellevue, WA (In Person)
Full-Time
Review key factors to help you decide if the role fits your goals.
Job Security
?
out of 5
Not enough data
Calculating job security score...
Total Score
100
out of 100
Average of individual scores
Skill Insights
Compare your current skills to what this opportunity needs—we'll show you what you already have and what could strengthen your application.
Job Description
Description At Amazon, we're building the most customer-centric company on earth and seeking exceptional talent to help create innovative capabilities for Amazon's Associates through People eXperience and Technology (PXT). Our Workforce Solutions team develops products that empower Amazon associates globally to enhance their careers through pay incentives, job mobility, and training opportunities, using modern technologies to solve complex human problems at scale while delivering intuitive, personalized experiences. We're looking for data engineers who are passionate about building large-scale data solutions, designing infrastructure for analyzing massive data volumes, solving complex problems using modern technology, and making direct impact on one of the world's most visited sites. At Amazon, customer obsession drives everything we do, and our data engineers tackle challenges that impact millions of customers, sellers, and products globally. We operate in a fast-paced environment where ship cycles are measured in weeks, not years. A key focus of this role involves harnessing the power of Generative AI to optimize data source discovery and pipeline development, building intelligent agents to help Business Intelligence Engineers and Scientists identify emerging opportunities, creating self-service capabilities for stakeholders, and developing AI-integrated data platforms. We seek curious minds who think big and want to define tomorrow. Join us in creating solutions that change the world. Key job responsibilities 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. Basic Qualifications
- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS Preferred Qualifications
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases) Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
- 132,100.00
- 178,800.