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.
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.
Qualifications
Master’s degree (or foreign equivalent) in Data Science, Data Analytics, Computer Engineering, Computer Science or a related field Requires completion of one graduate-level course, one research project, or one internship involving the following: Data ETL (Extract, Transform, Load) design, implementation, and maintenance on a large scale Data visualization via Tableau, R, or Python Programming in Hack, C/C++, Python, Perl, Java, or PHP Internet technologies: HTTP, HTML, CSS, or JavaScript Writing and optimizing SQL statements Analyzing large volumes of data to provide data driven insights, gaps, and inconsistencies Data governance standard and data privacy compliance Data processing automation Data warehousing architecture and plans Informatica, Talend, Pentaho, dimensional data modeling, or schema design Map Reduce or MPP system Machine Learning and Artificial Intelligence fundamentals Statistics methods: descriptive statistics, hypothesis testing, and regression analysis Distributed processing technologies and frameworks, such as Hadoop, and distributed storage systems (e.g., HDFS, S3) Spark programming: code writing, debugging and optimization