Staff, Software Engineer
Walmart · Chennai · 10+ yrs experience · Posted 2026-07-15
Tech stack: AWS, Azure, Docker, GCP, Kafka, Kubernetes, Python, Terraform
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About the role
This position is for the Replenishment operations team in US Omni Tech, where we perform SRE operations for 30 + Business critical applications ensuring that that right item with right quantity reached the stores at the right time. We also are moving on a very fast pace towards leveraging the power of AI in gaining high operational efficiency.
Responsibilities:
- Data Pipeline Reliability: Design, implement, and maintain scalable, fault-tolerant data pipelines processing high-volume datasets.
- Airflow & DAG Management: Manage Apache Airflow environments, optimize DAGs (Directed Acyclic Graphs), and ensure efficient scheduling, monitoring, and alerting for data workflows.
- Observability & Monitoring: Build and enhance observability platforms using tools like Prometheus, Grafana, ELK Stack, or similar to provide real-time insights into system health, performance, and data quality.
- Incident Management: Lead troubleshooting, root cause analysis, and post-incident reviews for data platform issues. Implement automated remediation strategies.
- Infrastructure as Code (IaC): Use tools like Terraform, Kubernetes, or Docker to manage and scale cloud-based data infrastructure (GCP/Azure).
- AI/ML Integration: Collaborate with data science teams to operationalize AI/ML models, ensuring reliable deployment, monitoring, and performance tuning.
- Proactive Optimization: Continuously improve system performance, cost efficiency, and reliability through automation, capacity planning, and chaos engineering practices.
- Documentation & Best Practices: Establish SRE standards, runbooks, and disaster recovery plans for data platforms.
Qualifications:
- 10+ years of Total IT experience and 5+ years of experience in SRE, DevOps, or data engineering roles with a focus on large-scale data systems.
- Strong proficiency in Python for automation, scripting, and tool development.
- Hands-on experience with Apache Airflow (DAG creation, management, scaling).
- Expertise in observability tools: Prometheus, Grafana, Datadog, Splunk, or similar.
- Experience with cloud platforms ( AWS, GCP, or Azure ) and container orchestration ( Kubernetes, Docker ).
- Knowledge of data pipeline technologies: Apache Spark, Kafka, Snowflake, BigQuery, or equivalent.
- Familiarity with AI/ML concepts and experience supporting ML pipelines in production.
- Strong understanding of SRE principles: SLIs/SLOs, error budgets, toil reduction, and blameless postmortems.
- Preferred:
- Experience with MLOps tools (MLflow, Kubeflow) and model monitoring.
- Knowledge of data governance, security, and compliance frameworks.
- Contributions to open-source projects or public tech talks/blog posts in relevant dom
Qualifications
- 10+ years of Total IT experience and 5+ years of experience in SRE, DevOps, or data engineering roles with a focus on large-scale data systems.
- Strong proficiency in Python for automation, scripting, and tool development.
- Hands-on experience with Apache Airflow (DAG creation, management, scaling).
- Expertise in observability tools: Prometheus, Grafana, Datadog, Splunk, or similar.
- Experience with cloud platforms ( AWS, GCP, or Azure ) and container orchestration ( Kubernetes, Docker ).
- Knowledge of data pipeline technologies: Apache Spark, Kafka, Snowflake, BigQuery, or equivalent.
- Familiarity with AI/ML concepts and experience supporting ML pipelines in production.
- Strong understanding of SRE principles: SLIs/SLOs, error budgets, toil reduction, and blameless postmortems.
- Experience with MLOps tools (MLflow, Kubeflow) and model monitoring.
- Knowledge of data governance, security, and compliance frameworks.
- Contributions to open-source projects or public tech talks/blog posts in relevant dom
Responsibilities
- Data Pipeline Reliability: Design, implement, and maintain scalable, fault-tolerant data pipelines processing high-volume datasets.
- Airflow & DAG Management:
- Manage Apache Airflow environments, optimize DAGs (Directed Acyclic Graphs), and ensure efficient scheduling, monitoring, and alerting for data workflows.
- Observability & Monitoring:
- Build and enhance observability platforms using tools like Prometheus, Grafana, ELK Stack, or similar to provide real-time insights into system health, performance, and data quality.
- Incident Management: Lead troubleshooting, root cause analysis, and post-incident reviews for data platform issues.
- Implement automated remediation strategies.
- Infrastructure as Code (IaC): Use tools like Terraform, Kubernetes, or Docker to manage and scale cloud-based data infrastructure (GCP/Azure).
- AI/ML Integration: Collaborate with data science teams to operationalize AI/ML models, ensuring reliable deployment, monitoring, and performance tuning.
- Proactive Optimization: Continuously improve system performance, cost efficiency, and reliability through automation, capacity planning, and chaos engineering practices.
- Documentation & Best Practices: Establish SRE standards, runbooks, and disaster recovery plans for data platforms.