Data Engineer, Product Analytics (University Grad)

Meta is seeking a Data Engineer in Product Analytics to design and scale data solutions that support growth, strategy, and an enhanced user experience across Meta's apps. This onsite role in New York, NY collaborates with software engineers, data scientists, and product managers to address data challenges at large scale. The compensation range for this role is USD 99,008 to 139,000 per year.

Responsibilities

• Oversee and implement data warehouse plans for a product line or group of products to address well-defined problems
• Identify the data required for business problems and implement the necessary logging to ensure data availability, coordinating with data infrastructure to triage issues and resolve
• Collaborate with engineers, product managers, and data scientists to understand data needs and present key insights in meaningful ways
• Build data expertise and apply controls to ensure privacy, security, compliance, data quality, and operational integrity for assigned areas
• Design, develop, and launch new data models and production-grade visualizations using standard toolkits
• Independently design, build, and deploy new data extraction, transformation, and loading processes in production, mentoring others on efficient queries
• Support existing production processes and implement optimized solutions with limited guidance
• Define and manage Service Level Agreements for data sets within ownership scope

Requirements

• Knowledge of SQL
• Programming knowledge in Python
• Knowledge of database systems
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Technologies

• SQL
• Python

Preferred Qualifications

• Curious, self-driven, analytical and eager to work with data
• Experience thriving in a fast-paced work environment
• Experience collaborating with individuals and organizations
• Demonstrated ability to integrate AI tools to optimize workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Ongoing AI skill development (e.g., prompt engineering, context management, agent orchestration) and staying current with emerging AI technologies