Overview

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.

Responsibilities

• Design, build, and operate large-scale batch and streaming data pipelines using Spark, Java, and Python, processing data at Walmart scale.
• Apply strong software engineering fundamentals - clean architecture, automated testing, CI/CD, code review discipline - to data engineering work; this is a software engineering role that specializes in data, not a script-writing role.
• Apply domain-oriented design, system design, and low-level design (LLD) rigor to ambiguous data and platform problems - evaluating dependencies, trade-offs, failure modes, scalability, cost, and long-term durability before writing a line of code.
• Flex into platform/software engineering work when needed: build and maintain services, APIs, and distributed systems that data pipelines depend on, contributing directly to GDF's broader platform codebase.
• Integrate AI, GenAI, and agentic capabilities directly into the data platform: embeddings/vector search for data discovery, LLM-assisted data quality and anomaly detection, agentic pipeline monitoring and self-healing, AI-assisted schema and lineage documentation.
• Partner with data scientists and ML engineers to build feature pipelines, model-serving data paths, and the data infrastructure that MLOps depends on.
• Own data quality, observability, and governance for the pipelines and datasets you build, with a strong Operational Excellence / Engineering Excellence discipline.
• Participate in and lead architecture and design reviews spanning both data engineering and platform engineering domains.
• As a Staff 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 data engineering standards.
• Continuously evaluate emerging Big Data, cloud, and AI/agentic technologies, adopting them pragmatically where they improve reliability, productivity, or business value.

Qualifications

• Genuinely hands-on coding: 8+ years of professional software engineering experience with significant hands-on data engineering at scale; strong proficiency in Java and Python is required, and strong working knowledge of Spark for distributed data processing is required, not optional. Additional experience with Go, C++, or Scala is a plus.
• 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.
• Deep experience with the Big Data ecosystem - Spark, Hadoop/HDFS, Hive, Kafka, workflow orchestration (Airflow or equivalent) - including batch and streaming processing, and large-scale data transformation.
• Strong data platform fundamentals: data modeling, ETL/ELT pipeline design, data quality and observability, data governance, and integration with lakehouse architectures (e.g., Delta Lake, BigQuery, Databricks).
• Full 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.
• Working literacy in platform engineering: microservices, API design, Kubernetes or equivalent orchestration, CI/CD, and cloud-native practices - enough to flex into platform/software engineering work confidently.
• Strong distributed systems fundamentals: event-driven patterns, service contracts, asynchronous processing, and fault tolerance at scale - paired with strong system design, domain-oriented design (DDD), and low-level design (LLD) skills, and demonstrated ability to evaluate trade-offs, failure modes, scalability, and cost when owning production code.
• Strong software engineering craftsmanship: testing strategy, code review standards, design patterns, and system design for high-throughput data systems.
• Proven ability to translate ambiguous data/business problems into clear technical execution plans, particularly within a newly formed team without fully established process.
• Strong ownership mindset and technical judgment; comfortable operating with high autonomy in a 0-to-1 environment.
• ...Outlined below are the required
• for this position. If none are listed, there are no
• Bachelor's degree in Computer Science and 4 years
• experience in software engineering or related field. Option 2: 6 years
• experience in software engineering or related field. Option 3:
• Master's degree in Computer Science and 2 years
• experience in software engineering or related field. 3 years
• experience in data engineering, database engineering, business intelligence, or business analytics.
• ...Outlined below are the optional
• for this position. If none are listed, there are no
• Primary Location...Block- 1, Prestige Tech Pacific Park, Sy No. 38/1, Outer Ring Rd Kadubeesanahalli , India

Benefits

• BelongingWe aim to create a culture where every associate feels valued for who they are, rooted in respect for the individual. Our goal is to foster a sense of belonging, to create opportunities for all our associates, customers and suppliers, and to be a Walmart for everyone. At Walmart, our vision is everyone included. By fostering a workplace culture where everyone is - and feels - included, everyone wins.
• Equal Opportunity EmployerWalmart, Inc., is an Equal Opportunities Employer - By Choice. We believe we are best equipped to help our associates, customers and the communities we serve live better when we really know them. That means understanding, respecting and valuing unique styles, experiences, identities, ideas and opinions while being inclusive of all people.