Software Engineer II, Marketplace Data Engineering

About the role

Nike is hiring a Software Engineer II for its Marketplace Data Engineering team. You will report to the Senior Engineering Manager and work on backend APIs, ontology layers and data pipelines that BI Experience teams and other GT domains consume. A Senior or Lead Engineer supports you with design and technical guidance.

Key responsibilities

• Help design and deliver the data products, APIs and pipelines that serve Marketplace and Nike Direct data needs across the enterprise.
• Build and own backend services that expose curated data to BI Experience teams and downstream GT domains.
• Contribute to high-throughput, low-latency, fault-tolerant data services with strong governance, row-level security and quality guarantees.
• Partner with product, analytics and BI Experience teams to design schemas, contracts and consumption patterns.
• Write clean, well-tested, well-documented code and stay current with data platforms, APIs and AI-assisted development.
• Drive continuous improvement in performance, scalability, reliability and observability, and collaborate with globally distributed teams on architecture, code quality and operations.
• Take end-to-end ownership of backend modules, from design through production support.

Required profile

• Minimum of 3-5 years of backend software development experience.
• Experience in a collaborative, multi-discipline team and with globally distributed teams.
• Bachelor's degree in Computer Science, Information Systems or a related field, or equivalent experience.
• Comfortable in a fast-paced, results-oriented environment and able to communicate clearly between business and technical audiences.

Required skills

• Microservices, domain-driven design and RESTful services in Java, Python, Scala or Node.
• Distributed/cloud environments (AWS, GCP or Azure).
• Data platforms (Databricks, Spark, Delta Lake or equivalent) and SQL at scale.
• Workflow orchestration (Airflow, Dagster or Prefect) and distributed messaging (Kafka, SQS or similar).
• CI/CD, unit testing, static analysis and automated integration tests (Jenkins, GitHub Actions).
• Test-driven development, including AI tools such as GitHub Copilot.
• Scrum and agile principles.
• Preferred: NoSQL and relational databases, monitoring/tracing tools, data modeling and serverless computing.