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# Software Engineer III

- **Company:** Wal-Mart Stores, Inc.
- **Location:** Sunnyvale, CA, United States
- **Salary:** $150,080.0 - $234,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Agile Methodology, Airflow, Amazon Web Services, JIRA, Automation of Tests, Microsoft Azure, Big Data, Databases, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Relational Databases, Distributed Computing Environment, Distributed Data Store, Github, Apache Hadoop, Hadoop Distributed File System, Python (Programming Language), PostgreSQL, Machine Learning, NoSQL, Object-Oriented Software Development, DataOps, Unstructured Data, Data Processing, Google Cloud, Cloud Platform System, Feature Engineering, Delivery Pipeline, Apache Spark, Model Validation, Parallel Computation, Backend, Information Technology, Integration Frameworks, Software Version Control, Data Pipelines, Docker
- **Published:** October 31, 2026
- **Apply:** https://www.jofdav.com/jobs/60352188-software-engineer-iii

## About the Role

Minimum education and experience required: Master's degree or equivalent in Computer Science, Engineering (any), or a related field. Position does not require specific years of experience but requires listed skills.

Skills Required: Experience designing and developing scalable backend systems and data processing frameworks using Python and Java. Experience architecting, implementing, and maintaining production-grade ETL pipelines using Java for large-scale data ingestion and transformation workflows. Experience designing distributed data systems that leverage parallel computing principles to optimize processing performance and resource utilization at scale. Experience applying object-oriented design patterns to build modular, maintainable, and extensible software components across data engineering platforms. Experience authoring, scheduling, and monitoring complex data pipeline orchestration workflows using Apache Airflow. Experience working with cloud-based infrastructure solutions including GCP, AWS, and Azure to support scalable ETL workloads and data processing services. Experience managing source code version control, collaborative development workflows, and containerized deployment pipelines
using GitHub and Docker. Experience building distributed data processing pipelines in Apache Spark and Java, utilizing big data frameworks including Hadoop and HDFS for high-volume data operations. Experience working with relational database systems including PostgreSQL, as well as NoSQL database technologies, for structured and unstructured data storage and retrieval. Experience developing Python-based analytical scripts to evaluate, validate, and benchmark machine learning model performance metrics. Experience applying machine learning concepts including feature engineering, model evaluation, and data warehousing principles to support data-driven decision-making systems. Experience managing end-to-end project task tracking and sprint-based delivery workflows using Agile methodologies and tools including Jira. Employer will accept any amount of graduate coursework, graduate research experience or professional experience with the required skills.

## Description

Duties: Design and develop data pipelines within the supply chain domain, enabling the generation of precise business metrics like demand forecasts. Identify and execute automation measures and enhancements to optimize pipeline's performance to maintain minimum running costs and adhere to the strict downstream SLAs. Collaborate closely with stakeholders to grasp diverse feature requirements, formulate technical specifications, and recommend designs and pipeline development at a substantial scale. Provide post-production support ensuring no downtime for our business serving pipelines. Configure the infrastructure essential for these pipelines. Collect and analyze extensive and intricate datasets. Disseminate vital metrics and datasets to relevant stakeholders. Collaborate with data scientists to devise and implement advanced feature engineering pipelines to serve complex production grade ML models and share insights for model accuracy. Ensure that best standards of data quality, sanctity,
security, and correctness is maintained in production grade pipelines by writing automation test, data validity scripts, and maintaining strict access policies.