Senior Manager, Software Engineering
Walmart · Bengaluru · 14+ yrs experience · Posted 2026-07-06
Tech stack: Java, Kubernetes, Python
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About the role
Walmart is a multinational, people-led, tech-powered omnichannel retailer with over 2.2M associates. At People Support Tech, EBS, we build products and platforms that support Walmart’s HR function and help associates get the support they need with clarity, speed, and minimal effort. Our team is responsible for transforming the People Help Center experience across web, voice, chat, and assisted-service channels.
Responsibilities:
- This is a critical, high-impact leadership role with significant growth opportunities. From day one, you will be trusted to drive engineering outcomes aligned with organizational goals and lead the evolution of modern, data-driven associate support experiences. Key responsibilities include, but are not limited to:
- Lead one or more engineering teams responsible for designing, building, deploying, and operating user-facing web, voice, and chat products that transform the People Help Center experience for Walmart associates.
- Own the engineering roadmap, technical execution, and delivery of scalable web applications, backend platforms, APIs, microservices, data services, and enterprise integrations.
- Provide engineering leadership across full-stack product development, including frontend experiences, backend services, service orchestration, data flows, platform integrations, operational tooling, and production support.
- Guide architecture and system design decisions across web platforms, distributed services, event-driven systems, enterprise integrations, data platforms, and support automation capabilities.
- Drive the design and evolution of data engineering capabilities that enable reliable data movement, knowledge retrieval, analytics, operational insights, and intelligent product experiences.
- Partner with product managers, designers, data engineers, data scientists, machine learning engineers, and platform teams to deliver customer-facing capabilities grounded in strong engineering, reliable data, and measurable outcomes.
- Lead the pragmatic integration of AI and GenAI capabilities into production systems where appropriate, including conversational support, knowledge retrieval, summarization, workflow assistance, recommendations, and decision-support features.
- Ensure systems are designed for scalability, reliability, performance, observability, security, privacy, accessibility, maintainability, and cost efficiency.
- Lead engineering execution across the full software development lifecycle, including design, coding, testing, CI/CD, deployment, monitoring, incident management, operational readiness, and ongoing maintenance.
- Champion DevOps, platform engineering, data engineering, and automation best practices to improve engineering velocity, reduce toil, strengthen release quality, and support continuous delivery.
- Establish strong engineering standards through architecture reviews, design reviews, code reviews, testing strategies, observability practices, production readiness reviews, and operational excellence mechanisms.
- Collaborate with security, privacy, legal, compliance, and responsible AI partners to ensure systems are safe, governed, monitored, auditable, and appropriate for enterprise use.
- Define and track engineering, operational, product, data quality, and AI quality metrics, including availability, latency, defect rates, deployment frequency, incident trends, data freshness, task success, user adoption, containment, cost, and customer satisfaction.
- Lead teams through complex technical trade-offs, balancing speed, quality, scalability, operational risk, cost, associate impact, and long-term platform strategy.
- Hire, mentor, coach, and develop engineers and engineering managers, creating a high-performing team culture grounded in ownership, craftsmanship, learning, inclusion, and continuous improvement.
- Build strong relationships with cross-functional stakeholders across engineering, product, design, data science, operations, HR, and business teams to align priorities, manage dependencies, remove blockers, and deliver measurable impact.
- Continuously evaluate emerging engineering, cloud, data, and AI technologies, applying them pragmatically where they improve customer experience, engineering productivity, reliability, or business value.
Qualifications:
- 14+ years of professional software engineering experience, with significant experience building and operating large-scale, user-facing web products, backend services, distributed systems, and enterprise platforms.
- 5+ years of engineering leadership experience, including leading high-performing engineering teams, managing engineers and/or engineering managers, and delivering complex technical programs across multiple workstreams.
- Bachelor’s or Master’s degree in computer science, engineering, or a related technical field, or equivalent practical experience.
- Proven experience building full-stack web-based products that serve real users at scale, with strong understanding of frontend, backend, API, data, platform, and operational considerations.
- Strong experience with microservices architectures, distributed systems, event-driven patterns, API design, service contracts, asynchronous processing, and enterprise system integration.
- Strong data engineering experience, including data modeling, data pipelines, batch and streaming processing, data quality, data observability, governance, and integration with enterprise data platforms.
- Experience building or leading data services that support search, knowledge retrieval, reporting, analytics, workflow automation, personalization, or decision-support capabilities.
- Practical experience integrating AI/ML or GenAI capabilities into software products, including model APIs, retrieval-based systems, automation, summarization, conversational interfaces, recommendation systems, or intelligent support workflows.
- Hands-on technical background in one or more modern engineering stacks such as Java, Python, and modern web/cloud technologies.
- Demonstrated experience leading teams that build production-grade backend services, web applications, data services, platform capabilities, and customer- or associate-facing digital experiences.
- Strong understanding of modern product engineering practices, including API design, frontend-backend integration, service ownership, telemetry, experimentation, release management, and production operations.
- Deep understanding of cloud-native engineering practices, including CI/CD, containerization, Kubernetes or equivalent orchestration platforms, infrastructure automation, telemetry, and production operations.
- Strong knowledge of reliability engineering, including observability, distributed tracing, logging, metrics, alerting, incident response, post-incident reviews, capacity planning, graceful degradation, and fault tolerance.
- Strong grounding in security, privacy, compliance, and enterprise engineering practices, including authentication, authorization, data protection, secure API design, vulnerability management, and audit readiness.
- Experience driving architecture reviews and making technical trade-offs across scalability, performance, maintainability, reliability, security, cost, data quality, and business outcomes.
- Proven ability to translate ambiguous business problems into clear technical strategies, execution plans, milestones, dependencies, and measurable outcomes.
- Strong product mindset, with experience partnering closely with product managers, designers, data engineers, data scientists, business stakeholders, and operations teams.
- Strong people leadership skills, including hiring, coaching, mentoring, performance management, succession planning, and creating a culture where engineers can do their best work.
- Demonstrated ability to develop senior engineers and engineering managers, build leadership depth, and scale engineering practices across multiple teams.
- Strong ownership mindset, sound technical judgment, high accountability, and ability to drive execution across complex programs while maintaining engineering quality.
Qualifications
- 14+ years of professional software engineering experience, with significant experience building and operating large-scale, user-facing web products, backend services, distributed systems, and enterprise platforms.
- 5+ years of engineering leadership experience, including leading high-performing engineering teams, managing engineers and/or engineering managers, and delivering complex technical programs across multiple workstreams.
- Bachelor’s or Master’s degree in computer science, engineering, or a related technical field, or equivalent practical experience.
- Proven experience building full-stack web-based products that serve real users at scale, with strong understanding of frontend, backend, API, data, platform, and operational considerations.
- Strong experience with microservices architectures, distributed systems, event-driven patterns, API design, service contracts, asynchronous processing, and enterprise system integration.
- Strong data engineering experience, including data modeling, data pipelines, batch and streaming processing, data quality, data observability, governance, and integration with enterprise data platforms.
- Experience building or leading data services that support search, knowledge retrieval, reporting, analytics, workflow automation, personalization, or decision-support capabilities.
- Practical experience integrating AI/ML or GenAI capabilities into software products, including model APIs, retrieval-based systems, automation, summarization, conversational interfaces, recommendation systems, or intelligent support workflows.
- Hands-on technical background in one or more modern engineering stacks such as Java, Python, and modern web/cloud technologies.
- Demonstrated experience leading teams that build production-grade backend services, web applications, data services, platform capabilities, and customer- or associate-facing digital experiences.
- Strong understanding of modern product engineering practices, including API design, frontend-backend integration, service ownership, telemetry, experimentation, release management, and production operations.
- Deep understanding of cloud-native engineering practices, including CI/CD, containerization, Kubernetes or equivalent orchestration platforms, infrastructure automation, telemetry, and production operations.
- Strong knowledge of reliability engineering, including observability, distributed tracing, logging, metrics, alerting, incident response, post-incident reviews, capacity planning, graceful degradation, and fault tolerance.
- Strong grounding in security, privacy, compliance, and enterprise engineering practices, including authentication, authorization, data protection, secure API design, vulnerability management, and audit readiness.
- Experience driving architecture reviews and making technical trade-offs across scalability, performance, maintainability, reliability, security, cost, data quality, and business outcomes.
- Proven ability to translate ambiguous business problems into clear technical strategies, execution plans, milestones, dependencies, and measurable outcomes.
- Strong product mindset, with experience partnering closely with product managers, designers, data engineers, data scientists, business stakeholders, and operations teams.
- Strong people leadership skills, including hiring, coaching, mentoring, performance management, succession planning, and creating a culture where engineers can do their best work.
- Demonstrated ability to develop senior engineers and engineering managers, build leadership depth, and scale engineering practices across multiple teams.
- Strong ownership mindset, sound technical judgment, high accountability, and ability to drive execution across complex programs while maintaining engineering quality.
Responsibilities
- This is a critical, high-impact leadership role with significant growth opportunities.
- From day one, you will be trusted to drive engineering outcomes aligned with organizational goals and lead the evolution of modern, data-driven associate support experiences.
- Key responsibilities include, but are not limited to:
- Lead one or more engineering teams responsible for designing, building, deploying, and operating user-facing web, voice, and chat products that transform the People Help Center experience for Walmart associates.
- Own the engineering roadmap, technical execution, and delivery of scalable web applications, backend platforms, APIs, microservices, data services, and enterprise integrations.
- Provide engineering leadership across full-stack product development, including frontend experiences, backend services, service orchestration, data flows, platform integrations, operational tooling, and production support.
- Guide architecture and system design decisions across web platforms, distributed services, event-driven systems, enterprise integrations, data platforms, and support automation capabilities.
- Drive the design and evolution of data engineering capabilities that enable reliable data movement, knowledge retrieval, analytics, operational insights, and intelligent product experiences.
- Partner with product managers, designers, data engineers, data scientists, machine learning engineers, and platform teams to deliver customer-facing capabilities grounded in strong engineering, reliable data, and measurable outcomes.
- Lead the pragmatic integration of AI and GenAI capabilities into production systems where appropriate, including conversational support, knowledge retrieval, summarization, workflow assistance, recommendations, and decision-support features.
- Ensure systems are designed for scalability, reliability, performance, observability, security, privacy, accessibility, maintainability, and cost efficiency.
- Lead engineering execution across the full software development lifecycle, including design, coding, testing, CI/CD, deployment, monitoring, incident management, operational readiness, and ongoing maintenance.
- Champion DevOps, platform engineering, data engineering, and automation best practices to improve engineering velocity, reduce toil, strengthen release quality, and support continuous delivery.
- Establish strong engineering standards through architecture reviews, design reviews, code reviews, testing strategies, observability practices, production readiness reviews, and operational excellence mechanisms.
- Collaborate with security, privacy, legal, compliance, and responsible AI partners to ensure systems are safe, governed, monitored, auditable, and appropriate for enterprise use.
- Define and track engineering, operational, product, data quality, and AI quality metrics, including availability, latency, defect rates, deployment frequency, incident trends, data freshness, task success, user adoption, containment, cost, and customer satisfaction.
- Lead teams through complex technical trade-offs, balancing speed, quality, scalability, operational risk, cost, associate impact, and long-term platform strategy.
- Hire, mentor, coach, and develop engineers and engineering managers, creating a high-performing team culture grounded in ownership, craftsmanship, learning, inclusion, and continuous improvement.
- Build strong relationships with cross-functional stakeholders across engineering, product, design, data science, operations, HR, and business teams to align priorities, manage dependencies, remove blockers, and deliver measurable impact.
- Continuously evaluate emerging engineering, cloud, data, and AI technologies, applying them pragmatically where they improve customer experience, engineering productivity, reliability, or business value.