Senior, Software Engineer
Walmart · Bengaluru · 6+ yrs experience · Posted 2026-06-12
Tech stack: GCP, Google Cloud, Java, Kubernetes, Scala, Terraform
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About the role
You will be part of an Engineering team that builds and operates scalable, intelligent data platforms enabling analytics, automation and AI-driven decision-making across the organization for Fintech space. Our work focuses on designing high-performance data systems, ensuring reliability and operational excellence and integrating Generative AI capabilities to enhance how data is processed, understood and used. We work at the intersection of data engineering and applied AI, developing production-grade agentic systems that handle data at scale, automate complex workflows, and drive measurable business impact
Walmart’s Enterprise Business Services (EBS) is a powerhouse of several exceptional teams delivering world-class technology solutions and services making a profound impact at every level of Walmart.
As a key part of Walmart Global Tech, our teams set the bar for operational excellence and leverage emerging technology to support millions of customers, associates, and stakeholders worldwide. Each time an associate turns on their laptop, a customer makes a purchase, a new supplier is onboarded, the company closes the books, physical and legal risk is avoided, and when we pay our associates consistently and accurately, that is EBS. Joining EBS means embarking on a journey of limitless growth, relentless innovation, and the chance to set new industry standards that shape the future of Walmart.
Responsibilities:
- Architect Data & application Systems at Scale: Design large-scale, distributed data architectures capable of processing massive datasets, integrating AI-driven workflows, and supporting real-time and batch processing needs.
- Drive System Design and Execution: Architect, develop, and deploy application and data processing engine — enabling autonomous orchestration of data pipelines, analytics, and business workflows.
- Lead End-to-End Productionization: Own the full lifecycle — build, containerize, deploy, and maintain apps across environments using Kubernetes, Terraform, and CI/CD systems, spark.
- Define and Enforce Engineering Standards: Set best practices around code quality, observability, scalability, and operational readiness for AI and data systems.
- Guide Data and Infrastructure Decisions: Lead design discussions for data modeling, system integrations, and infrastructure architecture on Google Cloud Platform (GCP) and related technologies.
- Enable Observability, Traceability & Reliability: Establish end-to-end monitoring and observability frameworks (e.g., Prometheus, Grafana, OpenTelemetry) for data services, ensuring deep traceability of agentic interactions.
- Collaborate Across Teams and Domains:Partner with Product, Data Science teams to align Gen AI initiatives with data engineering objectives — ensuring production reliability and scalability.
- Mentor and Grow Engineers: Provide technical mentorship to senior and mid-level engineers, fostering a culture of innovation, ownership, and operational excellence.
- Champion Operational Efficiency: Identify and automate pain points in deployment, scaling, and monitoring. Drive continuous improvement in system reliability, performance, and developer productivity.
- Own System-Level Decision Making: Evaluate new technologies, frameworks, and design patterns across LLMs and data infrastructure to guide the organization’s technical direction.
Qualifications:
- 6+ years of experience in Software Engineering, with significant focus on Data systems, integration, and distributed architectures.
- Deep expertise in Scala and distributed data frameworks like Spark data processing.
- Solid understanding of Scala or Java for high-performance and backend workloads.
- Expertise in Kubernetes (scaling, scheduling, service orchestration) and containerization practices.
- Proficiency with Terraform for cloud infrastructure provisioning and management.
- Deep understanding of CI/CD, automation, and cloud-native operations.
- Working knowledge of GCP (BigQuery, Cloud Run, Vertex AI, GCS).
- Strong experience designing for observability, monitoring, and traceability across microservices and AI agents.
- Knowledge on Airflow or equivalent for data orchestration.
- Familiarity with large language models (LLM)
- Experience with retrieval-augmented generation (RAG) systems
- Strategic mindset with the ability to balance technical depth and system-wide trade-offs.
Qualifications
- 6+ years of experience in Software Engineering, with significant focus on Data systems, integration, and distributed architectures.
- Deep expertise in Scala and distributed data frameworks like Spark data processing.
- Solid understanding of Scala or Java for high-performance and backend workloads.
- Expertise in Kubernetes (scaling, scheduling, service orchestration) and containerization practices.
- Proficiency with Terraform for cloud infrastructure provisioning and management.
- Deep understanding of CI/CD, automation, and cloud-native operations.
- Working knowledge of GCP (BigQuery, Cloud Run, Vertex AI, GCS).
- Strong experience designing for observability, monitoring, and traceability across microservices and AI agents.
- Knowledge on Airflow or equivalent for data orchestration.
- Familiarity with large language models (LLM)
- Experience with retrieval-augmented generation (RAG) systems
- Strategic mindset with the ability to balance technical depth and system-wide trade-offs.
Responsibilities
- Architect Data & application Systems at Scale: Design large-scale, distributed data architectures capable of processing massive datasets, integrating AI-driven workflows, and supporting real-time and batch processing needs.
- Drive System Design and Execution: Architect, develop, and deploy application and data processing engine — enabling autonomous orchestration of data pipelines, analytics, and business workflows.
- Lead End-to-End Productionization:
- Own the full lifecycle — build, containerize, deploy, and maintain apps across environments using Kubernetes, Terraform, and CI/CD systems, spark.
- Define and Enforce Engineering Standards: Set best practices around code quality, observability, scalability, and operational readiness for AI and data systems.
- Guide Data and Infrastructure Decisions:
- Lead design discussions for data modeling, system integrations, and infrastructure architecture on Google Cloud Platform (GCP) and related technologies.
- Enable Observability, Traceability & Reliability: Establish end-to-end monitoring and observability frameworks (e.g., Prometheus, Grafana, OpenTelemetry) for data services, ensuring deep traceability of agentic interactions.
- Collaborate Across Teams and Domains:Partner with Product, Data Science teams to align Gen AI initiatives with data engineering objectives — ensuring production reliability and scalability.
- Mentor and Grow Engineers:
- Provide technical mentorship to senior and mid-level engineers, fostering a culture of innovation, ownership, and operational excellence.
- Champion Operational Efficiency: Identify and automate pain points in deployment, scaling, and monitoring.
- Drive continuous improvement in system reliability, performance, and developer productivity.
- Own System-Level Decision Making: Evaluate new technologies, frameworks, and design patterns across LLMs and data infrastructure to guide the organization’s technical direction.