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 user experience across its apps. You will collaborate with software engineers, data scientists, and product managers to tackle data challenges at large scale. This onsite role in New York, NY offers a salary range of USD 99,008 to 139,000 per year.

Responsibilities

• Oversee and implement data warehouse strategies for a defined set of products to solve clearly scoped problems.
• Ascertain data requirements for business problems and implement the necessary logging to ensure data availability, coordinating with data infrastructure to triage and resolve issues.
• Collaborate with engineers, product managers, and data scientists to understand data needs and present key insights in a meaningful way.
• Develop data expertise and enforce data controls to ensure privacy, security, compliance, data quality, and operational readiness for assigned areas.
• Design, build, and launch new data models and production 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 your ownership scope.

Requirements

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

Technologies

• SQL
• Python

Preferred Qualifications

• Curious, self-driven, analytical and eager to work with data
• Ability to thrive in a fast-paced work environment
• Experience collaborating with individuals and cross-functional teams
• Proven ability to integrate AI tools to optimize workflows and drive measurable impact, such as efficiency gains or quality improvements
• Experience implementing responsible, ethical AI practices including risk assessment, bias mitigation, and quality reviews
• Ongoing AI skill development and staying current with emerging AI technologies, including prompt engineering and agent orchestration