McDonald's is looking to solve the problem of building scalable and efficient data solutions to support the company's data products and analytics initiatives, as part of its growth strategy, Accelerating the Arches.
Requirements
- 10+ years of strong experience in data engineering, preferably with cloud-based tech stack
- 7+ years of proficiency in programming languages commonly used in data engineering, such as Python.
- 5+ years of hands-on experience with big data processing frameworks, such as Apache Spark.
- 5+ years of hands-on experience with data modeling, ETL / ELT development, and data integration techniques.
- Experience with Google Cloud Platform (GCP) infrastructure
- Working knowledge of relational and dimensional data design and modeling in a large multi-platform data environment
- Solid understanding of SQL and database concepts.
Responsibilities
- Builds and maintains relevant and reliable data products that support the business needs. Develops and implements new technology solutions as needed to ensure ongoing improvement with data reliability and observability in-view.
- Owns engineering modules and functionalities and supports them through a full development cycle
- Leads a back-end engineering team and facilitates cross-functional relationships to solve relevant business issues
- Designing and developing data pipelines and integration processes to extract, transform, and load data from various sources into cloud-based data storage solutions
- Implementing and maintaining scalable data architectures that support efficient data storage, retrieval, and processing.
- Collaborating with data scientists and analysts to understand data requirements and ensure data accuracy, integrity, and availability.
- Building and optimizing data integration workflows to connect data from different systems and platforms.
Other
- Bachelor's or Master's degree in Computer Science or related engineering field
- Ability to drive continuous data management quality (i.e. timeliness, completeness, accuracy) through defined and governed principles
- Ability to perform extensive data analysis (comparing multiple datasets) using a variety of tools
- Excellent communication and collaboration skills to work effectively in cross-functional teams.
- Ability and flexibility to coordinate and work with teams distributed across time zones, as needed.
