Data Engineer, Analytics (Singapore)

About the role

Meta is hiring a Data Engineer, Analytics in Singapore. You will work with software engineering, data science and product management teams to design and build large-scale data solutions that support products used by billions of users and hundreds of millions of businesses. The role covers data architecture, logging, data models, security and governance, and product-influencing data insights.

Key responsibilities

• Own the data architecture for multiple large-scale projects, weighing design and operational cost-benefit trade-offs
• Contribute to logging frameworks and work with data infrastructure to triage and resolve issues
• Collaborate with engineers, product managers and data scientists to represent key data insights
• Define and manage Service Level Agreements for data sets in your area of ownership
• Determine the security model based on privacy requirements, confirm safeguards, address data quality issues and evolve governance processes
• Design, build and launch data models and visualizations supporting multiple use cases
• Solve data integration problems using optimal ETL patterns, frameworks and query techniques, from structured and unstructured sources
• Optimize pipelines, dashboards, frameworks and systems, and assist in maintaining production processes
• Influence product and cross-functional teams to identify data opportunities, and mentor team members

Required profile

• Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent
• 4+ years of experience primarily working with data (data analyst, data scientist, data engineer or similar)
• 4+ years of experience (or 2+ years with a Ph.D.) in SQL, ETL, data modeling and at least one programming language

Preferred qualifications

• Ongoing AI skill development and keeping up with emerging AI technologies
• Master's or Ph.D. in a STEM field
• Ability to integrate AI tools to improve workflows and drive measurable impact
• Experience applying responsible, ethical AI practices such as risk assessment, bias mitigation and quality reviews