Meta is looking to build a robust semantic layer for People data to facilitate seamless access, ensure data quality, integrity, and security, and drive faster speed to insights and analytics. A critical component is preparing data for AI applications to enable conversational analytics and AI-driven decision-making.
Requirements
- 5+ years of Advanced SQL, PL/SQL , Relational Database and data modelling
- 5+ Years of Data Architecture and data modelling skills set. Implemented / working knowledge of Kimball STAR Schema / Vault modelling techniques
- 3+ years of Visualization, Business Intelligence tools such as Tableau/Looker/Power BI
- 5+ years of QA / Testing and Data Analysis skills set. Experience to drive what if scenarios and advanced analytical skill set
- 4+ years of Data Stewardship experience, implementing data quality rules and metadata management
- 7+ years of experience in Data Governance and Data Management workstreams
- 3+ years of Python coding experience - building data pipelines
Responsibilities
- Develop and implement data architecture that complement data engineering design and policies, optimize querying processes to reduce time to insight. This includes designing data models, data warehousing solutions, and data integration frameworks that support the organization's data strategy
- Design and develop a holistic metrics aggregation and computation layer across people analytics, enabling the optimization of standard report production, enhancement of dashboard performance, and driving self-service analytics. This involves creating a unified framework for metric calculation, data aggregation, and data visualization
- Leverage subject matter expertise and business data process knowledge to design optimal data architecture that meets the needs of stakeholders. This requires a experience understanding of business operations, data flows, and data requirements
- Build ontology and AI-consumable data architecture and metadata, enabling the development of artificial intelligence and machine learning solutions. This involves creating data structures, taxonomies, and metadata frameworks that support AI applications
- Develop a comprehensive Data Governance framework and artifacts that provide a 360-degree view of a metric, supporting data literacy and catalog development. This includes creating data governance policies, procedures, standards, and guidelines that ensure data quality, security, and compliance
- Drive certification of metrics definitions and drive risk management ways of working in People Analytics. Drive Data Quality framework and implement Data Quality rules that is reactive and proactive. Validate data lineage and modernize data governance across People Analytics
Other
- Act as a bridge between the Business, People Analytics, and Engineering teams, empowering efficient decision-making by facilitating collaboration, communication, and data-driven insights
- Masters or PhD in Information systems, Computer Science, Engineering, Data Science or equivalent degree
- Knowledge of Semantic layer creation for BI tools
- 8+ years of Advanced SQL and developing last-mile delivery data architecture
