Data Engineer

Meta•Menlo Park, CA

17d

About The Position

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.

Requirements

Master's degree (or foreign equivalent) in Data Science, Computer Science, Mathematics or a related field
Requires completion of one graduate-level course, one research project, or one internship involving the following:
Programming in Python or PHP
SQL for data modeling and data ETL (Extract, Transform, Load) design, implementation, and maintenance on a large scale
Large-scale data handling (petabyte-scale) for identifying insights, data gaps, and inconsistencies
Online analytical processing (OLAP) and (online transaction processing) OLTP within data warehouse plus database systems
Hadoop, HBase, Hive, MySQL, and Scuba
Data logging and instrumentation design
Data processing automation, data quality, data warehousing, data governance, business intelligence, data visualization, and data privacy
Business intelligence tools

Responsibilities

Develop, 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.
Perform data analysis to generate business insights.
Interface with engineers, product managers and product analysts to understand product goals and data needs.
Build data expertise and own data quality for allocated areas of ownership.
Help manage data warehouse plans for a product or a group of products.
Support critical data processes running in production.