Data Engineer, Product Analytics (University Grad)

Meta is seeking a Data Engineer in Product Analytics at the university graduate level, onsite in Bellevue, WA. The role focuses on building scalable data solutions, models, and visualizations to answer product questions and drive growth across Meta’s apps. You will collaborate with software engineers, data scientists, and product managers to translate data into actionable insights. The annual compensation for this position ranges from USD 99,008 to 139,000.

Responsibilities

• Plan and implement data warehouse strategies for a product area or a group of products to address clearly defined problems
• Determine data requirements for business problems and establish the necessary logging to ensure data availability, coordinating with data infrastructure to triage and resolve issues
• Work with engineers, product managers, and data scientists to understand data needs and present key insights in accessible formats
• Develop domain data expertise and enforce data controls to maintain privacy, security, compliance, data quality, and operability for assigned areas
• Create and deploy new data models and visualizations in production using standard development toolkits
• Independently design and launch new data extraction, transformation, and loading processes in production, mentoring others on efficient querying
• Maintain and optimize existing production processes with limited supervision
• Define and monitor Service Level Agreements for datasets within ownership

Requirements

• Proficiency in SQL
• Programming ability 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 excited to work with data
• Experience thriving in a fast-paced work environment
• Proven ability to collaborate with individuals and organizations across teams
• Demonstrated capacity to integrate AI tools to optimize workflows and drive measurable impact such as efficiency gains and quality improvements
• Experience implementing responsible, ethical AI practices including risk assessment, bias mitigation, and quality and accuracy reviews
• Ongoing AI skill development, including prompt engineering and agent orchestration, with a commitment to staying current on emerging AI technologies