Data Engineer Ii, Supply Chain Analytics

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Data Science

This role is for a Back End Data Engineer focused on designing, developing, testing, and deploying Supply Chain Application and Process Automation within AWS Infrastructure Services. The engineer will collaborate with business leaders, BI engineers, and data scientists to architect solutions, identify automation opportunities, and build scalable applications using AWS technologies. A key responsibility is enhancing analytical maturity by incorporating predictive and prescriptive analytics using machine learning and optimization techniques, and designing data pipelines for ETL/ELT processes.

What you'd actually do

  1. Understand the broad range of organizational data resources and business processes to identify automation opportunities that drive measurable business outcomes.
  2. Interface with global stakeholders, data engineers, and data scientists across time zones to gather requirements by asking the right questions, analyzing data, and drawing conclusions through validated assumptions.
  3. Produce written recommendations and insights for key stakeholders to shape solution design and influence strategic decision-making.
  4. Conduct deep-dive analyses of business problems and formulate data-driven conclusions and recommendations to deliver comprehensive end-to-end automation solutions.
  5. Design, develop, and maintain scalable and reliable analytical tools, and automated pipelines that drive key business decisions across supply chain, procurement, and operational domains.

Skills

Required

  • 5+ years of data engineering experience
  • 5+ years of SQL experience
  • 3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
  • Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS

Nice to have

  • Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, K

What the JD emphasized

  • predictive and prescriptive analytics using machine learning and optimization techniques where appropriate