Data Engineer, Product Analytics (University Grad)

Meta is seeking a Data Engineer focused on product analytics to design scalable data solutions that support growth, strategy, and user experience across its apps. This role partners with software engineers, data scientists, and product managers to tackle data challenges at scale.

Location

New York, NY (onsite)

Salary

USD 99,008 - 139,000 per year

Responsibilities

• Plan and execute data warehouse initiatives for a product or group to address well-scoped problems.
• Determine data needs for business problems and implement 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 requirements and present key insights in a meaningful way.
• Develop data domain expertise and apply governance controls to ensure privacy, security, compliance, data quality, and operations within assigned areas.
• Create, deploy, and productionize new data models and visualizations using common development toolkits.
• Independently design and launch new ETL processes in production, mentoring others on efficient query practices.
• Support existing production processes and implement optimized solutions with limited guidance.
• Define and manage service level agreements for data sets within assigned domains.

Requirements

• Proficiency in SQL.
• Programming experience in Python.
• Familiarity with database systems.
• Must obtain work authorization for the country of employment at the time of hire and maintain it throughout employment.

Technologies

• SQL
• Python

Preferred Qualifications

• A curious, self-driven, analytical mindset with an enthusiasm for working with data.
• Experience thriving in a fast paced environment.
• Proven ability to collaborate with colleagues and cross-functional teams.
• Demonstrated capability to integrate AI tools to optimize workflows and drive measurable impact such as efficiency gains or quality improvements.
• Experience applying responsible, ethical AI practices including risk assessment, bias mitigation, and quality/accuracy reviews.
• Ongoing AI skills development, including prompt engineering and agent orchestration, with a commitment to staying current with new AI technologies.