Data Engineer, Product Analytics (University Grad)

Meta is seeking a University Graduate Data Engineer for Product Analytics to build scalable data solutions, models, and visualizations that answer product questions and support growth across Meta's apps. The role is onsite in Bellevue, WA, with an annual salary range of USD 99,008 to 139,000.

Responsibilities

• Plan and execute data warehouse initiatives for a product or product group to address clearly defined problems.
• Determine data requirements for business problems and implement necessary logging to ensure data availability, collaborating 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 domain data expertise and apply data governance controls to maintain privacy, security, compliance, data quality, and operational readiness for assigned ownership.
• Design, build, and deploy new data models and visualizations in production using standard development toolkits.
• Independently create and deploy new extraction, transformation, and loading processes in production and mentor others in writing efficient queries.
• Maintain and optimize existing production processes with minimal supervision.
• Define and manage service level agreements for datasets within owned areas.

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

Benefits

• Bonus
• Equity
• Benefits

Preferred Qualifications

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