Sr. Software Engineer

With a career at The Home Depot, you can be yourself and also be part of something bigger.

• Responsible for independently developing and consistently redesigning/refactoring a product to adapt to customers and associates needs.
• Be part of a dynamic team with engineers of all experience levels who help each other build and grow technical and leadership skills while creating, deploying, and supporting production applications.
• Be involved in product and tool selection, configuration, security, resilience, performance tuning and production monitoring as well as mentoring less experienced engineers.
• Develop, test, deploy, and maintain software, with a clear understanding of the value the software is to provide.
• Take on new opportunities and tough challenges with a sense of urgency, high energy and enthusiasm.
• Consistently achieve results, even under tough circumstances.
• Develop test suites (functional, destructive, etc.) to enable success, rapid deployment of code to production.
• Take a broad view when approaching issues, using a global lens.
• Learn through successful and failed experiments when tackling new problems.
• Actively seek ways to grow and be challenged using both formal and informal development channels.
• Collaborate with other team members in agile processes.
• Create new and better ways for the organization to be successful.
• Work with the Product Team to ensure user stories are valuable, developer ready, easy to understand, and testable.
• Deliver multi-mode communications that convey a clear understanding of the unique needs of different audiences.
• Adapt approach and demeanor in real time to match the shifting demands of different situations.
• Relate openly and comfortably with diverse groups of people.
• Help grow junior engineers by providing guidance on modern software development frameworks, and leading technical discussions.
• Telecommuting available.

Minimum Qualifications:

• Must be eighteen years of age or older.
• Must be legally permitted to work in the United States.
• Bachelor’s degree in Computer Science, Information Technology, Electronics Engineering, or a related field and 2 years of experience in a related occupation. Requires 2 years of experience in the following skills:
• Multi-cloud reliability strategies across GCP, Azure with deep specialization in GCP (Compute Engine, Google Kubernetes Engine (GKE), Cloud Run, App Engine, Cloud Storage, Cloud SQL, Bigtable, Memorystore, Vertex AI, BigQuery, Dataflow, Dataproc, Cloud Pub/Sub, IAM, Networking, VPC Network, Cloud DNS, Cloud NAT, Cloud Armor, Secret Manager, Cloud Trace, Cloud Monitoring, Cloud Logging) infrastructure, kubernetes multi tenancy and platform engineering.
• Designing and enforcing advanced SLO/SLI frameworks tied to business KPIs, including error budget policies that directly govern release velocity, change management, and production risk during peak retail or high-traffic events.
• Large-scale capacity engineering and peak readiness programs (e.g., Black Friday/Cyber Monday scenarios), including traffic modeling, failover strategies, and pre-production load simulation at global scale.
• Chaos engineering practices in distributed microservices environments using Kubernetes (GKE/EKS), with fault injection across network, compute, and dependency layers to validate resilience under real-world failure modes.
• Edge security and traffic management using Akamai, including WAF tuning, bot mitigation strategies, CDN performance optimization, and debugging complex HTTP routing behaviors impacting global user traffic.
• Building and operating full-stack observability platforms integrating Prometheus, Grafana, Datadog, New Relic, and Stackdriver, with custom telemetry pipelines, and intelligent alerting to reduce MTTR and eliminate alert fatigue.
• Developing Infrastructure-as-Code at scale using Terraform, including reusable modules, policy enforcement, and automated drift detection across multi-environment deployments.
• Designing and optimizing CI/CD pipelines (GitHub Actions, Cloud Build) with integrated reliability gates, canary analysis, and automated rollback strategies tied to SLO violations.
• Operating and scaling AI/ML production platforms, including model deployment pipelines, ML API reliability, latency optimization. Including handling GPU/CPU scarcity, model version rollbacks.
• Applying advanced debugging and root cause analysis techniques across distributed systems, including log correlation, trace analysis, and memory/thread profiling in high-concurrency Java-based services.
• Implementing secure authentication and authorization frameworks for distributed systems, including service-to-service identity, zero-trust principles, and integration with modern security standards.
• Relational and NoSQL databases, focusing on performance tuning, query optimization, and resilience under high-throughput conditions.
• Developing and executing comprehensive testing strategies, including unit, functional, destructive, and large-scale performance testing aligned with production traffic patterns.

• Modern software development practices, including object-oriented programming (Java, Python, Go-lang), frontend technologies (HTML, CSS, JavaScript/TypeScript), version control systems, and scalable design patterns for distributed applications.