Key skills
SQLAnalytics
About this role
Meta is seeking a highly skilled Data Engineer to support a large-scale data and analytics initiative. In this role, you will help enable data-driven decision-making by building scalable, reliable data infrastructure and developing well-governed metrics used across critical operational workflows.
Responsibilities:
- Design, build, and maintain scalable data pipelines and analytics-ready datasets supporting ENS operations
- Lead data warehouse design, including dimensional modeling, schema design, and optimization for large-scale analytical workloads
- Write and optimize complex SQL queries to support analytics, reporting, and downstream consumers
- Improve data reliability through data quality checks, monitoring, and validation frameworks
- Establish and enforce metrics definitions, data standards, and governance best practices
- Build dashboards, reports, and self-service analytics tools to empower teams with timely insights
- Partner with cross-functional teams to translate business requirements into robust, scalable data solutions
Requirements:
- Highly skilled Data Engineer
- Support a large-scale data and analytics initiative
- Build scalable, reliable data infrastructure
- Develop well-governed metrics used across critical operational workflows
- Design and deliver end-to-end data solutions
- Data warehouse architecture and modeling
- Automated data pipelines
- Analytics layers
- Dashboards
- Develop multi-level metrics
- Support financial and operational tracking
- Ensure high standards for data quality, consistency, and governance
- Collaborate with cross-functional partners
- Translate business requirements into durable data solutions
- Deliver trusted insights and self-service analytics
- Monitor performance, identify trends, and make informed decisions
- Design, build, and maintain scalable data pipelines
- Lead data warehouse design
- Dimensional modeling
- Schema design
- Optimization for large-scale analytical workloads
- Write and optimize complex SQL queries
- Improve data reliability through data quality checks, monitoring, and validation frameworks
- Establish and enforce metrics definitions, data standards, and governance best practices
- Build dashboards, reports, and self-service analytics tools
- Partner with cross-functional teams