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Do you want to use data to help drive the direction of products that impact the lives of over 1 billion people every day? If yes, we want to talk to you. We're looking for a leader in our Data Engineering team to work closely with Product Managers, Data Scientists and Software Engineers to support product launches and roadmaps by building the data architecture that informs and drives insight. In this role, you'll see a direct link between your work, company growth, and user satisfaction. You'll work with some of the brightest minds in the industry, work with one of the richest data sets in the world, use cutting edge technology, and see your efforts affect products and people on a regular basis. The perfect candidate will have demonstrated experience in data infrastructure and data architecture skills, a proven track record of leading and scaling BI teams, demonstrated experience in operational skills to drive efficiency and speed, project management leadership, and a comprehensive vision for how data can proactively improve companies.
Data Engineering Manager, Analytics Responsibilities
Proactively drive the vision for BI and Data Warehousing across a product vertical, and define and execute on a plan to achieve that vision.
Define the processes needed to achieve operational efficency in all areas, including project management and system reliability.
Build a high-quality BI and Data Warehousing team and design the team to scale.
Build cross-functional relationships with Data Scientists, Product Managers and Software Engineers to understand data needs and deliver on those needs.
Manage data warehouse plans across a product vertical.
Drive the design, building, and launching of new data models and data pipelines in production.
Drive data quality across the product vertical and related business areas.
Manage the delivery of high impact dashboards and data visualizations.
Define and manage SLAs for all data sets and processes running in production.

Were looking for a team leader to manage 2 data teams: our AI Analysts team and our Data Labeling team. The AI Analysts team manages end-to-end data processes for training and evaluating AI models. The Data Labeling team specializes in creating high-quality manual labels. In your day-to-day, you will:



Lead and oversee a team of 4 AI analysts while also managing the team lead who directs a separate team of 6 labeling experts

Drive professional development through mentorship while implementing structured processes to optimize productivity and workflow efficiency

Collaborate with cross-functional stakeholders to align data strategy with broader business objectives and AI initiatives

Establish quality standards and evaluation frameworks for data curation and model validation processes

Balance resource allocation across multiple concurrent projects while ensuring timely delivery of high-quality data assets

Take a significant role exploring innovative approaches to data curation and evaluation methodologies that advance our AI capabilities

We are looking for researchers and applied scientists to join the Central Applied Science team. Central Applied Science is an interdisciplinary team of quantitative scientists that aims to deliver research and innovation that fundamentally contribute to Meta's success. By applying your expertise in quantitative methods, you will be empowered to drive impact across a range of products, infrastructure and company operations. Individuals in this role are expected to have expertise and publications within research areas in the board field of machine learning. The ideal candidate will have a passion for building data-driven products and forming research frameworks to solve challenging, real-world problems.
Research Scientist, Central Applied Science Responsibilities
Work with vast amounts of data, generate research questions that push the state-of-the-art, and build data-driven products.
Develop novel quantitative methods on top of Meta's unparalleled data infrastructure.
Learn new tools, systems, and programming languages quickly as required by the particular project you are working on.
Work towards long-term ambitious research goals, while identifying intermediate milestones.
Communicate best practices in quantitative analysis to partners.
Work both independently and collaboratively with other scientists, engineers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value.
Publish research results in top-tier conferences and at leading journals.
Actively identify new opportunities within Meta's long term roadmap for applied science contributions.