Amazon Fresh Warehouse Jobs (NOW HIRING)

Join the OIS/CXI Analytics team to build strategic data infrastructure powering Amazon's Operations Technology ecosystem. Our team provides critical data infrastructure support for OpsTech IT, supporting Amazon's global customer commitment. You'll work at the intersection of large-scale data processing and real-world operational impact - creating intelligence that directly influences how Amazon fulfills millions of orders across fulfillment centers, Amazon Fresh, Prime Now, Lockers, Pantry, and Amazon Campus.
As a Data Engineer, you will build and maintain scalable data pipelines and ML-ready data infrastructure that power AI-driven operational insights and Data Science initiatives across Amazon's global fulfillment and maintenance networks. You will design and implement ETL/ELT pipelines, build feature engineering workflows, and partner with ML Engineers, Data Scientists, Applied Scientists, and BIEs to deliver data products that drive measurable business outcomes. You will contribute to MLOps data practices - including data versioning, pipeline monitoring, and model retraining data support - and help establish engineering best practices within the team. You will support Data Science teams by building curated, analysis-ready models and datasets and enabling self-service data access through well-governed data infrastructure. This role directly enables the team's mission to implement GenAI solutions for automated reporting, diagnostics, and predictive and prescriptive analytics across worldwide operations.
This is a high-impact individual contributor role with significant opportunity to grow technical scope and organizational influence at the intersection of data engineering, Data Science, and AI.
Key job responsibilities
- Design, build, and maintain production-grade ETL/ELT pipelines and big data infrastructure supporting OTS operational intelligence.
- Build feature engineering workflows and ML-ready data pipelines that support Data Science experimentation and production model serving.
- Contribute to data governance and quality standards across analytical and ML data products.
- Support implementation of GenAI solutions for automated reporting, diagnostic, predictive, and prescriptive analytics.
- Build and maintain semantic layers and dashboard data models that power worldwide operations business decisions.
- Partner with Program Managers, BI teams, ML Engineers, Data Scientists, and operational stakeholders to prioritize work aligned with OTS business goals.
- Follow and contribute to best practices for data engineering, including code reviews, testing, monitoring, and documentation.
A day in the life
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:
1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan
If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you're passionate about this role and want to make an impact on a global scale, please apply!
BASIC QUALIFICATIONS
- 3+ years of data engineering experience
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience in data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding
- Bachelor's degree or above in computer science, machine learning, engineering, or related fields, or experience including, building and maintaining data flows and pipelines
- Proficiency in Python and SQL; experience with PySpark or Apache Spark
- Experience with infrastructure-as-code (CDK, CloudFormation) and CI/CD pipelines for data and ML systemsExperience with data modeling and relational/non-relational database design
PREFERRED QUALIFICATIONS
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Master's degree or above in computer science, engineering, analytics, mathematics, statistics, IT or equivalent
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, 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.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, TN, Nashville - 125,500.00 - 169,800.00 USD annually
USA, TX, Austin - 132,100.00 - 178,800.00 USD annually