Responsibilities

• Integrate the semantic layer to serve as an AI-ready knowledge base, enabling applications such as advanced analytics, prompt engineering for large language models, and intelligent data discovery while ensuring seamless connectivity and holistic data understanding across the enterprise.
• Develop standards, guidelines, and best practices for knowledge representation, semantic modeling, and data standardization across DPR to ensure a clear and consistent approach within the enterprise semantic layer.
• Establish and refine operational processes for semantic model development, including intake mechanisms for new requirements (e.g., from AI prompt engineering initiatives) and backlog management, ensuring efficient and iterative delivery.
• Partner closely with analytics engineers and data architects to deeply understand the underlying data models in Snowflake and develop a profound understanding of our business domains and data entities. Provide strategic guidance on how structured data can be seamlessly transformed, optimized, and semantically enriched for advanced AI consumption and traditional BI/Analytics tools.
• Lead the effort to establish and maintain comprehensive documentation for all aspects of the semantic layer, which includes defining and standardizing key business metrics, documenting ontological definitions, relationships, usage guidelines, and metadata for all semantic models, ensuring clarity, consistency, and ease of understanding for all data users.
• Evaluate and monitor the performance, quality, and usability of semantic systems, ensuring they meet organizational objectives, external standards, and the demands of AI applications.
• Act as a thought leader, constantly evaluating emerging trends in knowledge graphs, semantic AI, prompt engineering, and related technologies to strategically enhance DPR’s capabilities in knowledge representation and data understanding.
• Rapidly prototype high-priority solutions in cloud platforms, demonstrating their feasibility and business value.
• Participate in all phases of the project lifecycle and lead data architecture initiatives.

Qualifications

• Proven expertise in data analysis, data modeling, and data engineering with a focus on cloud- native data platforms.
• 5+ years of hands-on experience in semantic modeling, ontology engineering, knowledge graphs, or related AI data preparation.
• 3+ years of experience developing data solutions specifically for AI/ML applications leveraging structured data.
• 3+ years of experience with data warehousing concepts, dimensional modeling, and data governance principles as they relate to structuring data for semantic enrichment.
• Proficiency in SQL and experience working with cloud-based data warehouses, preferably Snowflake.