Data Engineer, Worldwide Grocery Data & Analytics

DataJobs; Austin (TX); On-site; USD 101,000 - 160,000; Full time

Benefits offered by this job

Sign-on payments

RSUs

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Worldwide Grocery Store Tech (WWGST) is building the data foundation that supports data-driven decisions across grocery operations. You will design and operate data pipelines supporting GenAI/ML workloads, BI, and RAG, working closely with data engineers, BI engineers, and data scientists to ensure trusted, well-governed data.

This onsite role in Austin, TX centers on delivering dependable pipelines, robust data quality practices, and scalable infrastructure to empower analytics across the

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

Responsibilities

• Build the infrastructure that powers data-driven decision making using software engineering best practices and data management fundamentals.
• Manage and optimize cloud 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.
• Collaborate with BI 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 ML use cases.

Skills

Data modeling

Data warehousing

ETL pipelines

SQL

Python

KornShell

Education

Bachelor’s degree in Computer Science, Computer Engineering, Information Management, Information Systems, or related discipline

Tools

Hadoop

Hive

Spark

EMR

AWS

Amazon Q

Kiro

Job description

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 101,300 - 160,000 per year. You will work with data engineers, business intelligence engineers, and data scientists to deliver dependable pipelines and data quality practices at scale.

What you’ll do

• 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.

Core responsibilities for the data platform

• 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.

Requirements

• 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 KornShell
• Bachelor’s degree in Computer Science, Computer Engineering, Information Management, Information Systems, or another related discipline

Technologies you’ll use

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

About the team

• 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.

Benefits

• 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

Preferred qualifications

• 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