Senior, Software Engineer

About Team GDF (Global Data Foundation) is part of Walmart’s AI & Data organization - the team 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.

Our 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 across Walmart depend on every day.

What You’ll Do

• Design, build, and operate distributed platform services and APIs using Java and Python, applied against Kubernetes or equivalent cloud-native infrastructure.
• Flex into data engineering work when needed: contribute directly to Spark-based data pipelines and processing jobs, working comfortably in GDF’s data engineering codebase alongside platform work.
• Build AI, GenAI, and agentic-powered platform capabilities: intelligent developer tooling, workflow automation, agentic services, and AI-assisted operational tooling (monitoring, incident triage, capacity planning).
• Own platform reliability, scalability, and observability - service-level objectives, distributed tracing, alerting, and incident response for services you build, with a strong Operational Excellence / Engineering Excellence discipline.
• Guide architecture and system design decisions across microservices, event-driven systems, and enterprise integrations, with an explicit eye toward how platform choices affect downstream data engineering workloads.
• Partner with data engineers, data scientists, and ML engineers to ensure platform services expose the right primitives for data pipelines, feature stores, and model-serving paths.
• Establish engineering standards through architecture reviews, design reviews, code reviews, and production readiness reviews for a team building process from scratch.
• As a Senior engineer, set technical direction and best practices within your team and technical domain without needing a management title - mentor other engineers and influence GDF’s broader platform engineering standards.
• Continuously evaluate emerging platform, cloud, and AI/agentic technologies, adopting them pragmatically where they improve reliability, productivity, or business value.

What You’ll Bring

• Genuinely hands-on coding: 5-8 years of professional software engineering experience building and operating large-scale distributed platforms; strong proficiency in Java and Python is required - comfortable writing, reviewing, and debugging production code, not just directing others.
• Operational Excellence (OE) and Engineering Excellence (EE) mindset is a required skill, not a nice-to-have: SLO/SLA ownership, incident management and blameless post-incident reviews, proactive reliability and quality metrics, and continuous improvement built into how you ship, not bolted on afterward.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• Strong experience with microservices architectures, distributed systems, event-driven patterns, API design, service contracts, and enterprise system integration.
• Strong understanding of cloud-native engineering practices - CI/CD, containerization, Kubernetes or equivalent orchestration, infrastructure automation, and production operations.
• Strong exposure to Big Data / data engineering technologies - Spark required at a working level, with familiarity in Hadoop/HDFS, Hive, or Kafka - enough to flex into data engineering work confidently, not just integrate with it from a distance.
• Working exposure to AI and Agentic technologies: hands-on experience integrating model APIs and GenAI/LLM tooling, building or operating agentic workflows (tool-calling agents, multi-step autonomous systems), working with embeddings/vector search, and applying MLOps practices (feature stores, model lifecycle, retraining pipelines) - not just conceptual awareness.
• Strong knowledge of reliability engineering - observability, distributed tracing, logging, metrics, alerting, incident response, capacity planning, and fault tolerance for large-scale systems.
• Strong grounding in security, privacy, and enterprise engineering practices, including authentication, authorization, data protection, and secure API design.
• Ability to translate ambigu…