Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps and services like Messenger, Instagram, and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. To apply, click "Apply to Job" online on this web page.

Data Engineer Responsibilities

• Design, build, and launch data pipelines to move data across systems and build the next generation of data tools that generate business insights for a product.


• Analyze user needs and software requirements to determine workability and to offer support for end users on data usage.


• Design, architect, and develop software and data solutions that help product and business teams make data-driven decisions.


• Rethink and influence strategy and roadmap for building efficient data solutions and scalable data warehouse plans.


• Design, develop, test, and launch new data models and processes into production, and provide support.


• Leverage homegrown extract, transform, and load (ETL) framework as well as off-the-shelf ETL tools, as appropriate.


• Interface closely with data infrastructure, product, and engineering teams to build and extend cross platform ETL and reports generation framework.


• Identify data infrastructure issues and drive to resolution.

Minimum Qualifications

• Requires Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field. Foreign degree equivalent accepted. Requires completion of an undergraduate-level course, research project, or internship involving the following skills:


• 1. Features, design, and use-case scenarios across a big data ecosystem


• 2. Custom ETL design, implementation, and maintenance


• 3. Object-oriented programming languages


• 4. Schema design and dimensional data modeling


• 5. Writing SQL statements


• 6. Analyzing data to identify deliverables, gaps, and inconsistencies


• 7. Managing and communicating data warehouse plans to internal clients


• 8. MapReduce or MPP system


• 9. Python.

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