Senior Manager, Software Engineering

Position Summary...

What you'll do...

About Team GDF sits at the core of Walmart's AI & Data organization, building the foundational data platforms that make it possible for every team at Walmart — technical or non-technical — to build with trustworthy, well-governed, discoverable data.

Our platforms serve billions of transactions and interactions across 19 countries, powering everything from supply chain intelligence to customer-facing personalization to enterprise AI agents.

This particular team is newly formed to solve one of GDF's next big platform bets — which means you'll be shaping charter, operating model, and delivery cadence essentially from a blank page, not inheriting someone else's playbook.

What You'll 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.

What You'll Bring 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 re