Data Engineer, Worldwide Grocery Data & Analytics
AI in this role
You will design and operate pipelines that feed GenAI workloads, BI, including feature pipelines, embedding generation, and data ingestion for retrieval-augmented generation (RAG). You'll apply rigorous data quality practices — validation, deduplication, PII handling, and drift detection — to the training and retrieval datasets that our models depend on, recognizing that model quality is only as good as the data behind it.
Working with SQL and Python at depth, you will deliver performant, reliable transformations and use AI-assisted development tools to accelerate your work while validating correctness. You'll partner with data scientists, applied scientists, and analytics teams to make high-quality, well-governed data readily available at scale.
Key job responsibilities
- Help build the infrastructure that powers data-driven decision making, using software engineering best practices, data management fundamentals, data storage principles, and advances in distributed systems.
- Manage and optimize cloud computing resources, particularly on AWS, to support data infrastructure.
- Build and maintain reliable, well-tested data pipelines with strong data quality practices — validation, deduplication, PII handling, and monitoring — so consumers can trust the data.
- Use AI-assisted development tools (e.g., Amazon Q, Kiro etc.) to accelerate pipeline development, SQL, and debugging, while validating generated output for correctness.
- Collaborate with Business Intelligence Engineers on best practices for data reporting, analysis, integrity, testing, validation, and documentation.
- Partner with Data Scientists and analytics teams to make high-quality data available, laying groundwork to support future machine learning use cases.
- Contribute to architecture and technology decisions that enable a world-class user experience.
- Develop in-depth knowledge of Amazon's data sources to determine the appropriate resources for each use case.
- Thrive amid ambiguity, quickly building proofs of concept, iterating, and improving solutions.
- Create extensible, easy-to-maintain designs with a long-term vision.
- Align your work with business objectives to deliver value.
About the team
DASH Team is a cross-cutting data engineering team serving the broader WWGS organization and it is pioneering as an AI-first team, delivering analytics using AI in 2026. DASH operates as a central hub for data infrastructure, consuming raw data, transforming it into an analytical data, and enabling access for consumers across the grocery data community. The team focuses on building robust, scalable platforms, long-term infrastructure stability, leadership goals, and measurable impact.
Basic qualifications
- 2+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience writing and optimizing SQL queries with large-scale, complex datasets
- Experience with one or more scripting language (e.g., Python, KornShell)
- Bachelor's degree in Computer Science, Computer Engineering, Information Management, Information Systems, or other related discipline
Preferred qualifications
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Master's degree in Computer Science, Computer Engineering, Information Management, Information Systems, or other related discipline
- Experience using AI-assisted development tools in academic or professional settings
- Experience in RAG/LLM data integration
- Experience with prompt engineering fundamentals
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, TX, AUSTIN - 101,300.00 - 160,000.00 USD annually
How we score this
Data Engineer, Worldwide Grocery Data & Analytics at Amazon scores 65 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.
AI Level 3. The daily work is on or around AI systems, without necessarily building the model: remove AI and the job is hollow.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Describe a typical day in a role like this one: which parts run through AI directly?
- If you removed AI from this role, what would be left, and how do you decide what still needs a human?
Adapt your resume
- List these exact terms on your resume: Prompt Engineering and Rag. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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