data engineer retail execution

Summary

Data engineer designing and building cloud-based analytics platforms and pipelines for Procter & Gamble's retail execution, working with Python, Apache Spark, Databricks, and Azure in a hybrid model.

Описание

Procter & Gamble manufactures consumer goods and develops data and analytics cloud-based platforms and pipelines for business use.

Задачи

  • Design, develop, and implement data and analytics cloud-based analytics platforms (DAP) and pipelines that acquire, cleanse, transform, and publish data from a wide variety of sources;
  • Assemble large complex data sets that meet functional and non-functional business requirements;
  • Partner with data asset managers, architects, and development leads to ensure technical solutions provide data fit for use and aligned with architecture blueprints;
  • Contribute to and leverage coding standards and best practices to ensure services and components are efficient and reusable;
  • Identify, design, and implement internal process improvements.

Требования

  • Bachelor's degree in Computer Science or a related field;
  • Experience in data engineering, data science, machine learning, or a related field;
  • Proficiency with data architecture and data processing tools;
  • Proficiency in Advanced Python Programming, Design Patterns, and FastAPI;
  • Practical, hands-on experience with Apache Spark and Databricks;
  • Solid background in building and maintaining CI/CD pipelines, such as GitHub Actions;
  • Experience in cloud environment operations and deployments, such as Azure;
  • Familiarity with AI and ML concepts and frameworks;
  • Nice to have: Azure Associate Data Engineer (DP-203), Databricks Professional Data Engineer, Apache Spark Developer Associate, Professional Data Engineer - Google Cloud Platform (GCP) certifications.

Условия

  • Competitive starting salary and benefits program, including private health care, P&G stock, saving plans, and sport cards;
  • Regular salary increases and possible promotions based on results and performance;
  • Wide range of self-development possibilities, including training and certification paths;
  • Opportunity to change role every few years;
  • Hybrid work model with the option to work from home two days a week and time spent in the office;
  • Employment is exclusively extended on the basis of "Umowa o Pracę" (Full-time Employment Contract).

See also

Data Engineering jobs by country — openings, pay and top skills →

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