Data Engineer , QuBIT OPTIMA

- 3+ years of data engineering experience
- 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 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)
- 2+ years experience in building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets

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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.
Quantitative Business Intelligence Tools (QuBIT) is one of the largest and fastest-growing tech organizations within retail operations. Our mission is to deliver technical solutions that unify sources of truth, fill gaps, and reduce operational complexity. We invest in incremental solutions while working towards long-term objectives for our customers.

QuBIT OPTIMA is a data engineering and analytics team within QuBIT that supports identifying gaps in efficiency and builds supporting infrastructure and metrics to ensure benchmarks are met. The QuBIT OPTIMA team is developing data infrastructure to enable optimizing each process path and help GCF achieve its OP goals.

We are seeking an exceptional Data Engineer who is data-driven, detail-oriented, a continuous learner, and driven to assist our customers. You will work with other talented data engineers to build this data infrastructure using CDK and advanced technologies and libraries, including Spark, Ray, Open Lineage, Great Expectation, Polars, DQDL, and others, to improve performance and efficiency. As a member of the OPTIMA team, you will have the opportunity to be a thoughtful leader and work with a group of talented program managers, product managers, software engineers, BIEs, and business leaders.

Key job responsibilities
This position requires superior analytical thinking, ability to quickly approach large, ambiguous problems, and apply analytics, technical, and engineering expertise to rapidly analyze, validate, visualize, prototype, and deliver solutions. It also requires significant cross-functional work and integration with AFT, Finance, Process Engineering, and Program Management. You will also lead the implementation of another large-scale project to support the science team in improving inbound operations.

We are focused on building strong leaders within OPTIMA who can own and support these large-scale initiatives. We deal with data from all Amazon fulfillment centers across all regions, so the infrastructure required to support such large-scale data must be optimal and well-thought-out. If you are interested in learning new technologies and working in truly global and large scale data engineering environment, feel free to setup informational with the hiring manager.


About the team
QuBIT OPTIMA team's mission is to enable strategic-level decision-making, provide data-driven insights, propose actionable courses of action, and conduct targeted insight generation for Global Customer Fulfillment (GCF). Our goal is to empower operational leadership and support teams to identify and close gaps in operational performance.