Data Engineer, Analytics

Meta•Menlo Park, CA

2d•Remote

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 degree equivalent) in Computer Science, Analytics, Information Systems, Engineering, or a related field and 2 years of work experience in the job offered or related occupation.
Requires 2 years of experience in the following skills and technologies:
Data ETL (Extract, Transform, Load) design, implementation, and maintenance on a large scale
Programming in Python
Writing SQL statements
Analyzing large volumes of data to provide data driven insights, gaps, and inconsistencies
Data warehousing architecture and plans
Map Reduce
Hadoop
Hive
Shell scripting, Sqoop, Oozie, Spark, AWS-EC2, EMIR, S3, and Redshift and
Bigquery, Presto, Druid, and Tableau

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.
Telecommuting from anywhere in the U.S. allowed.