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Strong brand but niche Databricks/Spark data expertise reduces generalist applicant density.
Requires deep data-engineering and Databricks/Spark expertise, limiting cross-domain transferability.
Multiple mandatory technical, cloud, and leadership requirements create strict shortlisting filters.
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Lead the technical direction and architecture for a data product squad focusing on consumption layers, APIs, and data pipelines serving various Nike business areas.
Drive simplification and unification of data products to reduce operational and licensing costs while improving consistency and quality standards.
Own production reliability, scalability, security, and operational quality, including incident response and capacity planning for squad services.
7–10 years of backend and/or data engineering experience.
Proficiency in Java, Python, or Scala; strong SQL and data modeling skills.
Experience designing distributed systems and data products including microservices, REST/GraphQL APIs, event-driven architectures, batch and streaming pipelines.
Experience with Databricks, Spark, Delta Lake, Unity Catalog (or equivalent), Airflow, and cloud platforms (AWS, GCP, or Azure) including networking, IAM, and cost-aware architecture.
Proven leadership in technical design reviews, API and data contract definition, and consensus building across teams.
Strong mentorship experience with software engineers, including code reviews, design coaching, and career growth discussions.
Experienced with observability/SRE best practices and modern development workflows including TDD, CI/CD, and usage of AI engineering tools.