Data Engineer, Analytics

Meta•Menlo Park, CA

1d•$248,920 - $279,400•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

Bachelor's degree (or foreign degree equivalent) in Information Science, Computer Science, Engineering, Information Science, Mathematics, Statistics, Data Analytics, Applied Sciences, or a related field, and five years of progressive, post-baccalaureate work experience in the job offered or in a computer-related occupation
Requires five years of experience in the following:
Custom ETL design, implementation, and maintenance
Exploratory analysis across large and unstructured datasets
Data cleansing and normalization
Presentation with historical and realtime data visualizations
Object-oriented programming languages
Schema design and dimensional data modeling
Writing SQL statements and
Python or Java

Responsibilities

Design, model, and implement data warehousing activities to deliver the data foundation that drives impact through informed decision making.
Design, build and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains.
Collaborate with engineers, product managers and data scientists to understand data needs, representing key data insights visually in a meaningful way.
Define and manage SLA for all data sets in allocated areas of ownership.
Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve.
Determine and implement the security model based on privacy requirements, confirm safeguards are followed, address data quality issues, and evolve governance processes within allocated areas of ownership.
Solve challenging data integration problems utilizing optimal ETL patterns, frameworks, query techniques, and sourcing from structured and unstructured data sources.
Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts.
Influence product and cross-functional teams to identify data opportunities to drive impact.
Work on problems of diverse scope where analysis of data requires evaluation of identifiable factors.
Demonstrate good judgment in selecting methods and techniques for obtaining solutions.
Telecommuting from anywhere in the US allowed.