(IND) STAFF, DATA ENGINEER
Walmart · Posted Oct 29
Retail, e-commerce, grocery, pharmacy, and membership warehouse services
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About the role
GDF (Global Data Foundation) is part of Walmart's AI & Data organization, building the foundational data platforms that power AI, analytics, and decision-making across every Walmart banner and geography, from supply chain and merchandising to customer experience and enterprise AI agents. The newly created platform team is chartered with building next-generation data infrastructure at Walmart scale: reliable, high-throughput data pipelines, distributed processing systems, and platform capabilities that downstream teams depend on every day. This senior individual-contributor Staff Data Engineer role owns the pipelines, data models, and data platform decisions — building, integrating, and hardening production data infrastructure, reviewing the work of engineers around them, and making architectural calls around semantic and dimensional modeling, governance, and observability.
What you will do
- Problem Formulation: Translate business problems into data-driven solutions, choosing the right methods and tools and explaining how they solve the problem.
- Business Context: Turn business requirements into concrete projects with clear ROI, acting as the bridge between business needs and technical solutions.
- Data Source Identification: Identify and vet the best-fit internal/external data sources against SLAs and quality standards, and guide junior engineers on sourcing decisions.
- Data Transformation & Integration: Design and automate ETL/ELT pipelines across diverse sources, deploy them via orchestration frameworks, and catch data-quality risks early.
- Data Modeling: Build and maintain logical/physical data models across relational, dimensional, and NoSQL platforms, and set modeling standards the team can reuse.
- Code Development & Testing: Review designs and code, choose the right languages and testing approach, fix scalability issues, and keep production stable.
- Data Governance: Establish and implement data-governance practices, interpret policy for the team, and keep documentation and standards current.
- Data Strategy: Apply the org's data strategy to moderately complex, cross-functional business problems.
- Lead the design of scalable conceptual, logical, and physical data models for operational, analytical, and AI/ML use cases.
- Define and implement dimensional models — conformed dimensions, fact tables, SCD Type 1 and Type 2 patterns, snapshots, event models, and data-product contracts.
- Design and maintain a governed semantic layer that standardizes business metrics, calculations, hierarchies, joins, definitions, and security policies across BI, self-service analytics, and AI applications.
- Establish modeling standards for naming, grain, keys, lineage, audit columns, partitioning, clustering, retention, and data-quality controls.
- Partner with domain teams to translate business processes into reusable, certified datasets and semantic models.
- Build and optimize transformation pipelines using SQL and modern data-engineering frameworks.
- Enable self-service analytics through certified metrics, reusable dimensions, governed views, and documentation.
- Drive data-governance practices including data classification, row- and column-level security, privacy controls, access patterns, metadata, and lineage.
- Review architecture and data-model designs, identify risks, and guide teams toward scalable, maintainable solutions.
- Define quality and observability measures — reconciliation, freshness, completeness, metric consistency, and schema-change monitoring.
- Mentor engineers and analysts in data modeling, semantic design, and engineering best practices.
- Influence enterprise data architecture strategy and technology decisions across data platforms, BI tools, and AI applications.
Skills used in this role
What the employer is looking for
- 12+ years of professional data engineering experience (or equivalent demonstrated impact). Semantic and dimensional modeling expertise is a core, must-have requirement for this role — not a nice-to-have.
- Deep, hands-on expertise in dimensional data modeling — facts, dimensions, grain, surrogate keys, conformed dimensions, SCD Type 1/2 patterns, and snapshot modeling.
- Proven track record designing and governing semantic models and certified metrics for BI and self-service analytics platforms, including modeling tools such as Erwin.
- Extensive experience building logical and physical data models across relational, dimensional, and NoSQL platforms, with modeling standards other engineers can reuse.
- Strong hands-on expertise in Python and SQL.
- Production experience with Apache Spark, Apache Hive, and Scala for large-scale data processing.
- Hands-on experience with Azure and Google BigQuery (GBQ) cloud data platforms.
- Experience building, deploying, and operating pipelines with orchestration frameworks (e.g., Airflow, Dagster, or equivalent).
- Hands-on experience with Apache Kafka or equivalent for real-time streaming data pipelines.
- Proven experience implementing data quality frameworks and ensuring compliance and data governance across the data lifecycle.
- Demonstrated ability to review and guide the work of other engineers, even without formal management authority.
- Demonstrated ability to lead cross-functional technical initiatives and influence senior engineering and business stakeholders.
- Strong stakeholder communication skills — comfortable translating business asks into technical solutions and back.
- Experience working with Agentic AI frameworks (e.g., LangChain, LangGraph, AutoGen) to build autonomous or LLM-driven data workflows.
- Working knowledge of CI/CD practices and automated deployment pipelines (e.g., Jenkins, GitHub Actions, Azure DevOps).
Preferred qualifications
- Strongly preferred: hands-on experience with semantic-layer or BI technologies such as Looker/LookML, Cube, AtScale, Power BI, or Tableau to deliver governed, certified metrics.
- Experience designing data products for AI, machine learning, agentic applications, or retrieval-based systems.
- Experience with healthcare, retail, financial, or other regulated data domains.
- Experience implementing privacy-aware data access patterns, including masking, policy tags, authorized views, and role- or attribute-based access controls.
- Experience with event-driven data, CDC pipelines, streaming platforms, or lakehouse architectures.
- Familiarity with data catalog, lineage, observability, and data-quality platforms.
- Working knowledge of Java or React.js — preferred.
Benefits and support
- Incentive awards for performance
- Maternity and parental leave
- Paid time off (PTO)
- Health benefits
About Walmart
Walmart Inc. is an American multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores. As one of the world's largest companies by revenue, it serves millions of customers daily through physical stores and robust e-commerce platforms. The company is driven by a commitment to everyday low prices and an expanding suite of digital and convenience-focused services.
- Industry
- Retail
- Company size
- 2000000+ employees
- Founded
- 1962
- Location
- Bentonville, Arkansas, USA
- Funding stage
- Public Company
Leadership
President and CEO
Executive Vice President and Chief Financial Officer
Executive Vice President, Global Chief Technology Officer and Chief Development Officer
Executive Vice President and Chief Growth Officer
President and CEO, Walmart U.S.
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