Senior Manager, Software Engineering
Walmart Global Tech · Posted Oct 14
Software development, cloud infrastructure, and retail technology services for Walmart
IN TN CHENNAI Home Office Capita LandFull-timeOnsiteLead/Staff12+ years₹65.0L–₹95.0L yearly<25 applicants
Information TechnologyRetail TechnologySoftware DevelopmentCloud ComputingArtificial IntelligencePublic Company
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About the role
GDF sits at the core of Walmart's AI & Data organization, building foundational data platforms that enable trustworthy, well-governed, and discoverable data across Walmart. The platforms serve billions of transactions and interactions across 19 countries, powering supply chain intelligence, personalization, and enterprise AI agents. This newly formed team is solving one of GDF's next big platform bets, and the role will lead engineering for core data platform capabilities including pipelines, processing frameworks, storage layers, and service APIs, with pragmatic integration of AI and GenAI.
What you will do
- Lead the engineering team(s) responsible for designing, building, and operating GDF's core data platform capabilities - pipelines, processing frameworks, storage layers, and service APIs that power downstream analytics and AI workloads across Walmart.
- Stay hands-on: write and review production code, prototype solutions to hard technical problems, and participate directly in design and code reviews rather than delegating all technical depth to the team.
- Own the engineering roadmap, technical execution, and delivery of scalable, high-throughput data services built on languages/frameworks including Java, Python, and Spark.
- Guide architecture and system design decisions across distributed data processing systems, batch and streaming pipelines, data lake/lakehouse storage, and enterprise integrations.
- Drive the design and evolution of Big Data capabilities - data ingestion, transformation, quality, observability, and governance - at the scale Walmart's platforms require.
- Partner with product managers, data scientists, ML engineers, and platform/security teams to deliver reliable, well-governed data capabilities that enable downstream analytics, reporting, and AI/ML use cases.
- Lead pragmatic integration of AI and GenAI capabilities into the data platform - e.g., feature pipelines for ML models, embeddings/vector search for retrieval, LLM-assisted data quality checks, and intelligent pipeline monitoring - wherever they create measurable engineering or business value.
- Ensure systems are designed for scalability, reliability, performance, observability, security, and cost efficiency, appropriate for petabyte-scale enterprise data platforms.
- Lead engineering execution across the full software development lifecycle - design, coding, testing, CI/CD, deployment, monitoring, incident management, and ongoing operational excellence.
- Champion DevOps, platform engineering, and automation best practices to improve engineering velocity, reduce toil, and support continuous delivery for a newly formed team building process and standards from scratch.
- Establish engineering standards through architecture reviews, design reviews, code reviews, testing strategy, observability practices, and production readiness reviews.
- Define and track engineering and data quality metrics - availability, latency, pipeline SLAs, data freshness, defect rates, deployment frequency, incident trends, and cost efficiency.
- Hire, mentor, and develop hands-on engineers, building a high-performing team culture grounded in craftsmanship, ownership, and continuous learning.
- Build strong cross-functional relationships across engineering, product, data science, and business teams to align priorities, manage dependencies, and remove blockers.
- Continuously evaluate emerging Big Data, cloud, and AI/ML technologies, applying them pragmatically where they improve platform reliability, engineering productivity, or business value.
Skills used in this role
JavaPythonSparkHadoopHDFSHiveKafkaDelta LakeBigQueryDatabricksKubernetesCI/CDmicroservicesdistributed systemsAPI designcloud-nativecontainerizationinfrastructure automationobservabilitydistributed tracingMLOpsembeddingsvector searchLLMGenAI
What the employer is looking for
- 12+ years of professional software engineering experience, including significant hands-on experience building and operating large-scale data platforms, distributed systems, and backend services.
- 4+ years of engineering leadership experience, including managing engineers and leading technical teams through complex delivery programs.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- Genuinely hands-on: comfortable writing, reviewing, and debugging production code - not just directing others. Strong proficiency in Java and Python is required; working knowledge of Spark for distributed data processing is expected.
- Strong exposure to Big Data technologies and ecosystems - Spark, Hadoop/HDFS, Hive, Kafka, or equivalent - including batch and streaming data processing, data pipeline design, and large-scale data transformation.
- Experience with data platform fundamentals: data modeling, ETL/ELT pipeline design, data quality and observability, data governance, and integration with enterprise data lakes/lakehouses (e.g., Delta Lake, BigQuery, Databricks).
- Strong experience with microservices architectures, distributed systems, event-driven patterns, API design, and enterprise system integration.
- Deep understanding of cloud-native engineering practices - CI/CD, containerization, Kubernetes or equivalent orchestration, infrastructure automation, and production operations.
- Strong knowledge of reliability engineering - observability, distributed tracing, logging, metrics, alerting,
- incident response, capacity planning, and fault tolerance for large-scale data systems.
- Full exposure to AI technologies in a production data platform context - hands-on experience integrating model APIs and GenAI/LLM-based tooling, working with embeddings/vector search, and applying MLOps practices (feature stores, model lifecycle, retraining pipelines). Comfortable using AI to strengthen data quality checks, pipeline monitoring, and engineering productivity, not just aware of it conceptually.
- Proven ability to translate ambiguous business/technical problems into clear execution plans, especially in a newly formed team without established process.
- Strong people leadership skills - hiring, coaching, mentoring, performance management - and a track record of building engineering culture from the ground up.
- Strong ownership mindset, sound technical judgment, high accountability, and comfort operating in a 0-to-1 environment.
Benefits and support
- Incentive awards for performance
- Maternity and parental leave
- PTO
- Health benefits
About Walmart Global Tech
Walmart Global Tech is the technological powerhouse and engineering arm of Walmart Inc., responsible for building and managing foundational technologies, cloud infrastructure, and software systems. The organization powers global retail operations across Walmart U.S., Sam's Club, and Walmart International, delivering customer experiences and enterprise services worldwide. Operating across multiple global hubs, it unites engineering, data science, product design, and artificial intelligence to digitally transform retail.
- Industry
- Information Technology
- Company size
- 25000+ employees
- Founded
- 2011
- Location
- Bentonville, Arkansas, USA
- Funding stage
- Public Company
Leadership
SK
Suresh Kumar
Executive Vice President, Global Chief Technology Officer and Chief Development Officer
DD
Daniel Danker
Executive Vice President, AI Acceleration, Product and Design
SR
Sanjay Radhakrishnan
Senior Vice President and Tech Operating Partner, Walmart International