Role Overview
The Data Engineer is responsible for designing, developing, and implementing data and analytics cloud-based analytics platforms (DAP) and pipelines that acquire, cleanse, transform, and publish data from a wide variety of sources.
Key Responsibilities
- Assemble large complex data sets which meet functional and non-functional business requirements.
- Partner with data asset managers, architects, and development leads to ensure a technical solution provides data which is fit for use and in line 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.
Work Environment
At Procter & Gamble, we embrace a hybrid work model that combines the flexibility of remote work with the collaborative benefits of in-office engagement. Employees can enjoy the option to work from home two days a week while also spending time in the office to foster teamwork and enhance communication.
What We Offer
- P&G-sized projects and access to world leading IT partners and technologies from Day 1.
- Wide range of self-development possibilities (training and certifications paths).
- Competitive starting salary and benefits program (private health care, P&G stock, saving plans, sport cards).
- Regular salary increases and possible promotions - in line with your results and performance.
- Opportunity to change role every few years to be in the best place for you and best for P&G.
Job Qualifications
- Bachelor's degree in Computer Science or related field.
- Experience in data engineering, data science, machine learning or 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 (e.g., GitHub Actions) complemented by experience in cloud environment operations and deployments (e.g., Azure).
- Familiarity with AI and ML concepts and frameworks.
- Certifications: Azure Associate Data Engineer (DP-203), Databricks Professional Data Engineer, Apache Spark Developer Associate, Professional Data Engineer - Google Cloud Platform (GCP).
