Data Engineering Manager, Analytics Monetization
Meta
Job Overview
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Job Description
About Meta
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps 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. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
The Opportunity: Data Engineering Manager, Analytics Monetization
Meta is seeking a leader in our Data Engineering team to work closely with Product Managers, Data Scientists and Software Engineers to support product launches and roadmaps by building the data architecture that informs and drives insight. In this role, there will be a direct link between your work, company growth, and user satisfaction. You'll work with some of the brightest minds in the industry, leverage one of the richest data sets in the world, use cutting-edge technology, and see your efforts affect products and people on a regular basis.
Responsibilities
- Drive the mission and strategy for Business Intelligence (BI) and Data Warehousing across a product vertical.
- Build and lead a high-quality BI and Data Warehousing team, designing it to scale.
- Develop cross-functional relationships with stakeholders to understand data needs and deliver on those needs.
- Manage data warehouse plans, drive data quality, and ensure operational efficiency.
- Design, build, and launch new data models and pipelines in production.
- Deliver high-impact dashboards and data visualizations.
- Define and manage Service Level Agreements (SLAs) for all data sets and processes running in production.
- Drive efficiency and speed, project management leadership, and a vision for how BI can proactively improve companies.
Minimum Qualifications
- A minimum of 4 years of work experience (2+ years with a Ph.D.) in applied analytics, including a minimum of 2 years of experience managing analytics teams.
- 8+ years of experience in BI and Data Warehousing.
- Bachelor of Arts/Bachelor of Science in Computer Science, Math, Physics, or other technical fields.
- Experience initiating and completing BI, data warehousing and/or analytical projects with minimal guidance.
- Experience communicating results of analysis to executive leadership.
- Project management experience.
- Data architecture experience.
- Experience with data querying languages (e.g., SQL) and development experience in at least one object-oriented language (Python, Java, etc.).
- 2+ years of experience managing people with experience scaling and managing 3+ person teams.
- Experience working closely on cross-functional teams, including Data Engineering, Data Science, Software Engineering, and Product Management.
Preferred Qualifications
- Experience with large data sets, Hadoop, and data visualization tools.
- Experience in a consumer web or mobile company.
- Advanced degree.
Key skills/competency
- Data Engineering
- Business Intelligence (BI)
- Data Warehousing
- Analytics Monetization
- SQL
- Python/Java
- Data Architecture
- Project Management
- Cross-functional Leadership
- Data Visualization
How to Get Hired at Meta
- Research Meta's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Tailor your resume: Highlight data engineering leadership, analytics monetization, and large-scale data architecture experience for Meta.
- Showcase impact: Quantify your achievements in BI, data warehousing, and managing high-performing analytics teams.
- Prepare for technical deep-dives: Brush up on SQL, Python/Java, data modeling, Hadoop, and data visualization tools relevant to Meta.
- Emphasize cross-functional collaboration: Provide concrete examples of successfully working with Product Managers, Data Scientists, and Software Engineers.
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