Data Engineer for Shelf Analytics MŁ

We are looking for an experienced Data Engineer to join the Shelf Analytics project – a data-driven application designed to analyze how P&G products are positioned on store shelves. The primary objective of the solution is to improve product visibility, optimize in-store execution, and ultimately increase sales by combining shelf layout data with sales insights.

As a Data Engineer, you will play a key role in building, maintaining, and enhancing scalable data pipelines and analytics workflows that power shelf-level insights. You will work closely with analytics and business stakeholders to ensure high-quality, reliable, and performant data solutions.

• Responsibilities:

  Design, develop, and maintain data pipelines and workflows using Databricks and PySpark
  Read, understand, and extend existing codebases; independently develop new components for Databricks workflows
  Implement object-oriented Python solutions (classes, inheritance, reusable modules)
  Develop and maintain unit tests to ensure code quality and reliability
  Work with Spark SQL and SQL Server Management Studio to create and optimize complex queries
  Create and manage Databricks workflows, clusters, databases, and tables
  Handle data storage and access management in Azure Data Lake Storage (ADLS), including ACL permissions
  Collaborate using GitHub, following CI/CD best practices and working with GitHub Actions
  Support continuous improvement of data engineering standards, performance, and scalability

• Mandatory Skills Description:

  Strong programming skills in Python and PySpark
  Hands-on experience with Databricks (workflows, clusters, tables, databases)
  Solid knowledge of SQL and experience with Spark SQL and SQL Server Management Studio
  Experience with pandas, dbx, and unit testing frameworks
  Practical experience working with Azure Storage (ADLS) and access control (ACLs)
  Proficiency with GitHub, including CI/CD pipelines and GitHub Actions
  Ability to work independently, analyze existing solutions, and propose improvements

• Nice-to-Have Skills Description:

  Experience with retail, CPG, or shelf analytics–related solutions
  Familiarity with large-scale data processing and analytics platforms
  Strong communication skills and a proactive, problem-solving mindset