Data Engineer, Grocery Data & Analytics

Worldwide Grocery Store Tech (WWGST) is building the data foundation that supports data-driven decisions and advanced analytics across grocery operations. As part of the Data & Analytics team, you will design and operate robust infrastructure and data pipelines that enable generative AI and machine learning workloads, BI, and retrieval-augmented generation (RAG). The DASH team serves as a cross-cutting hub for data engineering, transforming raw data into analytical datasets and helping data consumers access trusted, well-governed information.

This onsite role is located in Austin, TX, and supports a salary range of USD 101, per year
. You will work with data engineers, business intelligence engineers, and data scientists to deliver dependable pipelines and data quality practices at scale.

• Build the infrastructure that powers data-driven decision making using software engineering best practices, data management fundamentals, data storage principles, and distributed systems concepts.
• Manage and optimize cloud computing resources, with a focus on AWS
  , to support data infrastructure.
• Create reliable, well-tested data pipelines with strong data quality practices including validation, deduplication, PII handling, and monitoring.
• Use AI-assisted development tools such as Amazon Q and Kiro to accelerate pipeline development, SQL work, and debugging, while validating generated output for correctness.
• Collaborate with Business Intelligence Engineers on reporting, analysis, integrity, testing, validation, and documentation best practices.
• Partner with Data Scientists and analytics teams to provide high-quality data and support future machine learning use cases.
• Contribute to architecture and technology decisions that enable a world-class user experience.
• Develop deep knowledge of Amazon data sources to select appropriate resources for each use case.
• Work effectively in ambiguous environments by creating proofs of concept, iterating, and improving solutions.
• Design extensible, easy-to-maintain systems with a long-term vision and align delivery with business objectives.

• Develop and run pipelines that feed GenAI workloads
  , BI, feature pipelines, embedding generation, and data ingestion for RAG
  .
• Apply rigorous data quality practices including validation, deduplication, PII handling, and drift detection for training and retrieval datasets.
• Deliver performant, reliable SQL and Python transformations while validating correctness.

• 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 at least one scripting language such as Python or Korn Shell
• Bachelor’s degree in Computer Science
  , Computer Engineering
  , Information Management
  , Information Systems
  , or another related discipline

• SQL, Python, Korn Shell
• AWS
• Amazon Q, Kiro
• Hadoop, Hive, Spark, EMR

• The DASH Team is a cross-cutting data engineering group serving the broader WWGS organization.
• DASH is described as an AI-first team, delivering analytics using AI in 2026.
• The team operates as a central hub that consumes raw data, transforms it into analytical data, and enables access for consumers across the grocery data community.
• DASH focuses on robust, scalable platforms, long-term infrastructure stability, leadership goals, and measurable impact.

• Sign-on payments
• Restricted stock units (RSUs)
• Health insurance including medical, dental, vision, prescription, Basic Life & AD&D, with option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, and Adoption and Surrogacy Reimbursement coverage
• 401(k) matching
• Paid time off
• Parental leave

• Experience with big data technologies:
  Hadoop, Hive, Spark, EMR
• Master’s degree in Computer Science, Computer Engineering, Information Management, Information Systems, or related discipline
• Experience using AI-assisted development tools in academic or professional settings
• Experience in RAG/LLM data integration
• Experience with prompt engineering fundamentals