Principal, Software Engineer

Overview

Principal Software Engineer on Walmart's newly created GDF (Global Data Foundation) platform team, building next-generation data infrastructure at Walmart scale that downstream teams depend on daily.

What you'll do

• 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.
• 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 code.
• Flex into platform/software engineering work when needed: build and maintain services, APIs, and distributed systems that data pipelines depend on, contributing to GDF's broader platform codebase.
• Integrate AI, GenAI, and agentic capabilities 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 data engineering and platform engineering domains.
• Set technical direction and best practices across multiple teams and the broader GDF platform without a management title; mentor other engineers and influence GDF's data engineering standards.
• Continuously evaluate emerging Big Data, cloud, and AI/agentic technologies, adopting them pragmatically where they improve reliability, productivity, or business value.

What you'll need

• 12+ years of professional software engineering experience with significant hands-on data engineering at scale.
• Strong proficiency in Java and Python (required).
• Strong working knowledge of Spark for distributed data processing (required, not optional).
• Operational Excellence (OE) and Engineering Excellence (EE) mindset (required skill): SLO/SLA ownership, incident management and blameless post-incident reviews, proactive reliability and quality metrics, continuous improvement built into how you ship.
• 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, and 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).
• Working literacy in platform engineering: microservices, API design, Kubernetes or equivalent orchestration, CI/CD, and cloud-native practices.
• Strong distributed systems fundamentals: event-driven patterns, service contracts, asynchronous processing, and fault tolerance at scale, paired with 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 across a newly formed platform organization without fully established process.
• Strong ownership mindset and technical judgment; comfortable operating with high autonomy in a 0-to-1 environment.
• Minimum qualifications, Option 1: Bachelor's degree in computer science, computer engineering, computer information systems, software engineering, or related area and 5 years' experience in software engineering or related area.
• Minimum qualifications, Option 2: 7 years' experience in software engineering or related area.

Nice to have

• Additional experience with Go, C++, or Scala.
• Master's degree in computer science, computer engineering, computer information systems, software engineering, or related area and 3 years' experience in software engineering or related area.
• Knowledge of inclusive digital experiences, including Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and digital accessibility best practices.

Details

• Location: Bangalore, Karnataka, India (Building 10, Cessna Business Park, Kadubeesanahalli Village, Varthur Hobli).
• Work mode: based in the Bangalore office for daily work, with flexibility for associates to manage personal lives.