What you'll do:
• Design, build, test, and deploy scalable, intelligent data solutions that support millions of Sam’s Club customers - while laying the groundwork for Agentic AI systems that consume and act on this data.
• Partner with engineering, AI/ML, and product teams to ensure data services are discoverable, trustworthy, and consumable by next-gen AI agents and LLM-based systems.
• Collaborate across Sam’s Club engineering teams to contribute to a data-first, agent-aware architecture and foster a culture of innovation around intelligent automation.
• Engage with Product Management and Business teams to prioritize data products that will drive autonomous decision-making and context-rich recommendations.
• Build features that enable seamless integration between structured/unstructured data and downstream AI agents, enabling smarter responses, faster insights, and automation.
• Deploy and monitor products on cloud platforms with agent observability, telemetry, and auditability in mind.
• Develop and implement best-in-class data health monitoring, traceability, and context enrichment processes to ensure data used by agents is reliable and governed.
• Lead technical solutioning for full-lifecycle projects, with an eye on how data will power autonomous workflows, co-pilots, and multi-agent systems.
What you'll bring:
• 4–6 years of experience in Big Data development with a focus on scalable, fault-tolerant architectures.
• 2–3 years of hands-on experience with cloud platforms such as GCP or Azure, including services like BigQuery, Dataflow, Pub/Sub, or equivalent.
• Strong foundation in data engineering best practices and experience building complex data pipelines optimized for agent consumption.
• Experience designing and implementing semantic layers or knowledge graphs that could power data-aware AI agents.
• Proven experience in data modeling and architecture, with awareness of how data structures affect retrieval quality and contextual relevance for agents.
• Exposure to LLM-driven workflows, prompt templating, or orchestration tools (e.g., LangChain, LlamaIndex, CrewAI) is beneficial but not required.
• Understanding of data governance, including quality, access control, and lineage - especially in the context of agent auditability and trust.
• Experience writing clean, testable code in Python, Java, or Scala; experience with PySpark/Spark for distributed data processing.
• Demonstrated ability to write optimized, scalable SQL and to work with large datasets across cloud-native and open-source platforms.
• Familiarity with tools like Kafka, Spark Streaming, Druid, and Presto, and how they interact in real-time or hybrid data systems.
• Solid software engineering experience across backend and frontend development.
• Advanced experience with Java and Spring Boot, along with strong experience using modern frontend frameworks such as React, Angular, or Vue.js.
• Experience working with SQL (Azure SQL) and NoSQL (Cosmos, Cassandra, MongoDB) databases in both backend services and Full Stack systems.
• Hands-on experience with Kafka, Docker/Kubernetes, and cloud platforms such as Azure, and GCP.
• A strong understanding of DevOps principles, CI/CD pipelines, system observability, and deployment patterns for applications with both frontend and backend components.
• A demonstrated ability to design and maintain scalable, reliable, high-performing Full Stack applications.
• Success building enterprise-level systems that include both user-facing interfaces and backend services.
• Strong experience with API design, performance optimization, and scalable solution development across the full stack.