Data Engineer, Product Analytics (University Grad)

As a Data Engineer at Meta, you will shape the future of people-facing and business-facing products we build across our entire family of applications (Facebook, Instagram, Messenger, WhatsApp, Reality Labs, Threads). Your technical skills and analytical mindset will be utilized designing and building some of the world’s most extensive data sets, helping to craft experiences for billions of people and hundreds of millions of businesses worldwide.In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user experience for our 3 billion plus users, as well as our internal employee community.You will be at the forefront of identifying and solving some of the most interesting data challenges at a scale few companies can match. By joining Meta, you will become part of a world-class data engineering community dedicated to skill development and career growth in data engineering and beyond.Data Engineering: You will guide teams by building optimal data artifacts (including datasets and visualizations) to address key questions. You will refine our systems, design logging solutions, and create scalable data models. Ensuring data security and quality, and with a strong focus on efficiency, you will suggest architecture and development approaches and data management standards to address complex analytical problems.Product leadership: You will use data to shape product development, identify new opportunities, and tackle upcoming challenges. You’ll ensure our products add value for users and businesses, by prioritizing projects, and driving innovative solutions to respond to challenges or opportunities.Communication and influence: You won’t simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner., 1. Manage and execute data warehouse plans for a product or a group of products to solve well-scoped problems

1. Identify the data needed for a business problem and implement logging required to ensure availability of data, while working with data infrastructure to triage issues and resolve
2. Collaborate with engineers, product managers and data scientists to understand data needs, representing key data insights in a meaningful way
3. Build data expertise and leverage data controls to ensure privacy, security, compliance, data quality, and operations for allocated areas of ownership
4. Design, build and launch new data models and visualizations in production, leveraging common development toolkits
5. Independently design, build and launch new data extraction, transformation and loading processes in production, mentoring others around efficient queries
6. Support existing processes running in production and implement optimized solutions with limited guidance
7. Define and manage Service Level Agreements for data sets in allocated areas of ownership

Data Engineer, Product Analytics (University Grad) Responsibilities, 9. Currently has, or is in the process of obtaining a Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta

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

Preferred Qualifications:

Preferred Qualifications:

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