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Mid-level experience, remote role, metro location, and generalist data engineering skills increase competition.
Data engineering skills are transferable, but SCM domain knowledge increases industry specificity.
Mandatory 3–6 years plus specific Databricks, PySpark, CI/CD, and SCM domain skills.
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Design, develop, and operate scalable batch and streaming data pipelines in Azure Databricks for Supply Chain Management (SCM) using PySpark and/or Scala.
Implement and maintain high-quality data layers and products following lakehouse architecture with automated testing, CI/CD integration, and version control.
Collaborate cross-functionally with business stakeholders, IT partners, and data teams globally to ensure data availability, quality, and alignment to SCM business requirements.
Degree in Computer Science, Data Engineering, Information Systems, or related discipline.
3–6 years of hands-on data engineering experience in enterprise environments building production-grade pipelines.
Proven expertise in Azure Databricks, PySpark and/or Scala, version control, CI/CD, and test-driven development practices.
Experience in Supply Chain Management (SCM) domain or a comparable business domain.
Experienced in operating within agile, product-team structures embedded in enterprise-scale Azure environments with global delivery footprints.
Capable of translating business requirements into technical data contracts and pipeline designs aligned to governance and metadata standards.
Comfortable collaborating with international teams across multiple time zones, especially India, Germany, and the Philippines.