You have a deep interest and passion for technology. You have a passion to drive critical business initiatives with Data. You love writing and owning codes and enjoy working with people who will keep challenging you at every stage. You have strong problem solving, analytic, decision- making and excellent communication with interpersonal skills. You are self-driven and motivated with the desire to work in a fast-paced, results-driven agile environment with varied responsibilities. About Team: Sam's Club is our membership warehouse club, a business model that provides our members with high-quality products at prices that are unrivaled by traditional retail. Sam's Club provides a carefully curated assortment of items, as well as developing and leading technologies and services such as Scan & Go, Club Pickup, and home delivery service in select markets. Sam's Club also provides travel, auto purchasing, pharmacy, optical, hearing aid centers, tire and battery centers, and a portfolio of business operations support services. 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.
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Job Type
Full-time
Career Level
Mid Level