Staff, Software Engineer
Walmart Global Tech · Posted Oct 28
Software development, cloud infrastructure, and retail technology services for Walmart
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About the role
The Reliability Engineering and Operations (REO) Reporting & Analytics Team sits within Walmart's Developer Tools and Infrastructure organization, driving operational excellence and system reliability through actionable insights, predictive analytics, and automated reporting. The team is building intelligent reliability platforms that combine real-time observability data, incident analytics, and AI-driven automation across thousands of services. This role owns full-stack reliability and analytics applications end-to-end, including dashboards, backend platform capabilities, and LLM/RAG/MCP-based AI agent workflows for incident triage and root cause analysis.
What you will do
- Design, develop, and own full-stack reliability and analytics applications end-to-end — from responsive, data-rich front-end interface like React to scalable backend services (Node.js, Python, Java/Spring Boot) and robust data layers — delivering seamless developer and SRE experiences
- Build interactive, real-time dashboards and self-service analytics portals with modern front-end frameworks, enabling engineering teams to visualize reliability metrics, incident trends, and operational health at a glance
- Design and own core backend platform capabilities — RESTful and GraphQL APIs, data ingestion pipelines, metric aggregation engines, and AI-powered anomaly detection systems — enabling SRE and engineering teams to gain actionable reliability insights with minimal friction
- Architect and implement AI agent-based workflows powered by large language models (LLMs) for automated incident triage, root cause analysis, and predictive alerting across Walmart's infrastructure
- Build and evolve Retrieval-Augmented Generation (RAG) pipelines that connect LLMs to operational knowledge bases — runbooks, incident histories, post-mortems, and monitoring data — enabling intelligent, context-aware troubleshooting and decision support
- Design and integrate Model Context Protocol (MCP) servers to seamlessly connect AI agents with enterprise observability tools, incident management systems, monitoring APIs, and internal data platforms for end-to-end automated workflows
- Define and implement the platform's data and semantic layer, unifying heterogeneous operational data sources (metrics, logs, traces, incidents, change records) into a coherent, queryable representation for both human analysts and AI agents
- Establish and uphold non-functional requirements — query latency SLAs, data freshness guarantees, pipeline throughput targets, and availability contracts — as first-class platform standards for all reporting and analytics services
- Own observability and explainability as platform primitives — structured logging, audit trails, response attribution, and governance metadata that make AI-generated insights trustworthy, auditable, and actionable in production
- Build predictive analytics models and automated reporting systems that proactively surface reliability risks, capacity trends, and performance degradation patterns before they impact customers
- Partner across SRE, platform engineering, product, and data science teams to translate operational data into reliable, well-documented APIs and dashboards that internal teams can build against confidently
- Mentor engineers, lead design reviews, and build a culture of data-driven reliability — shared analytics infrastructure, stable interfaces, and reusable patterns over per-team duplication
- Track, analyze, and provide guidance to the team for resolving critical tech debts and improving engineering excellence metrics across the SDLC
- Deploy and manage containerized applications and data pipelines using Docker and Kubernetes at Walmart scale
- AI Engineering & Integration: Leverage AI-assisted development tools such as GitHub Copilot, ChatGPT, Claude, or similar tools to improve software development productivity, code quality, testing, debugging, and documentation
- Generative AI: Develop and integrate Generative AI solutions, including LLM-based applications, prompt engineering, and AI-powered developer and operational tooling
- RAG & MCP: Build and maintain Retrieval-Augmented Generation (RAG) applications and Model Context Protocol (MCP) servers to integrate LLMs with enterprise observability data, incident management tools, APIs, and external systems
Skills used in this role
What the employer is looking for
- 10+ years of software engineering experience, with meaningful depth in full-stack development, platform engineering, data infrastructure, observability systems, or reliability/SRE tooling
- Proven full-stack expertise — strong proficiency in modern front-end frameworks like React and back-end technologies (Node.js, Python/FastAPI/Flask, Java/Spring Boot, or Go), with the ability to own features from UI to database
- Hands-on experience designing and shipping production LLM-based systems — agent orchestration, tool use, RAG pipelines, multi-step reasoning, MCP integrations, or structured output generation
- Strong experience with data engineering and analytics platforms — building ETL/ELT pipelines, working with data warehouses (BigQuery, Snowflake, or equivalent), and designing real-time and batch reporting systems
- Strong software engineering fundamentals — clean API design (REST, GraphQL), distributed systems patterns, async execution, fault tolerance, and observability
- Proficiency in JavaScript/TypeScript and Python, with familiarity in the AI/ML and data engineering ecosystem; comfort reasoning about latency, throughput, and cost tradeoffs in data-intensive and inference-heavy workloads
- Experience with relational databases (PostgreSQL, MySQL), NoSQL stores (MongoDB, Cassandra), and caching layers (Redis) in production environments
- Experience with monitoring and observability tools (Prometheus, Grafana, Splunk, Datadog, or equivalent) and understanding of SRE principles (SLOs, SLIs, error budgets)
- Deep appreciation for architectural principles — separation of concerns, dependency inversion, layered design, and enforcement of contracts through code rather than convention
- Experience designing extensible plugin or extension systems with stable, versioned contracts and well-defined conformance expectations
- Experience owning and operating multi-tenant analytics systems with strict data isolation and access control requirements
- Excellent communication skills with the ability to translate complex operational data into clear, actionable insights for diverse stakeholders (engineers, SREs, leadership)
Benefits and support
- incentive awards for your performance
- maternity and parental leave
- PTO
- health benefits
About Walmart Global Tech
Walmart Global Tech is the technological powerhouse and engineering arm of Walmart Inc., responsible for building and managing foundational technologies, cloud infrastructure, and software systems. The organization powers global retail operations across Walmart U.S., Sam's Club, and Walmart International, delivering customer experiences and enterprise services worldwide. Operating across multiple global hubs, it unites engineering, data science, product design, and artificial intelligence to digitally transform retail.
- Industry
- Information Technology
- Company size
- 25000+ employees
- Founded
- 2011
- Location
- Bentonville, Arkansas, USA
- Funding stage
- Public Company
Leadership
Executive Vice President, Global Chief Technology Officer and Chief Development Officer
Executive Vice President, AI Acceleration, Product and Design
Senior Vice President and Tech Operating Partner, Walmart International
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