Data Architect

LatentView Analytics is a leading global analytics and decision sciences provider, delivering solutions that help companies drive digital transformation and use data to gain a competitive advantage. With analytics solutions that provide a 360-degree view of the digital consumer, fuel machine learning capabilities, and support artificial intelligence initiatives., LatentView Analytics enables leading global brands to predict new revenue streams, anticipate product trends and popularity, improve customer retention rates, optimize investment decisions, and turn unstructured data into valuable business assets.

About the role

We're looking for an AI Data Lead who can own the full arc — from translating business problems into Data blueprints, to guiding delivery teams, to presenting the right data to senior stakeholders. Equal parts strategist, architect, and communicator.

What you'll do

Snowflake Architecture & Optimization

• Design and implement scalable, high-performance data architectures on Snowflake to support enterprise analytics, ML workloads, and Gen AI applications.
• Optimize data storage, compute usage, query performance, and cost efficiency across Snowflake environments.
• Establish architecture standards, best practices, and reusable patterns for data ingestion, transformation, and serving.

AI-Ready Data & Knowledge Architecture

• Architect end-to-end AI-ready data platforms — going beyond pipelines to design the full Knowledge Architecture: knowledge maps, multimodal data representation (Vector DBs, Knowledge Graphs), and context assembly strategies.
• Design data structures and retrieval mechanisms that directly power agentic AI behaviors, including context retrieval, memory, and reasoning workflows.
• Integrate feedback loops, evaluation frameworks, and observability layers to monitor and continuously improve AI system performance at the data layer.
• Build and manage vector stores, knowledge graphs, and hybrid retrieval systems to support LLM and RAG-based applications.

Semantic Layer & Data Modeling

• Design and implement robust semantic models and metrics layers that provide consistent, governed, and business-friendly data access across BI and AI tools.
• Develop dimensional and semantic data models that serve both traditional analytics and AI/ML consumption patterns.
• Own the abstraction of complex business logic into reusable semantic definitions, ensuring single source of truth across the organization.

Collaboration & Governance

• Partner closely with data scientists, AI engineers, and analysts to operationalize ML models and AI use cases on a solid data foundation.
• Ensure data governance, security, lineage, and compliance best practices are embedded into every layer of the data platform.
• Drive data cataloging and metadata management to improve discoverability and trust across data assets.

What we're looking for

Core Experience

• 10–12 years of hands-on experience in data engineering and architecture, with deep, proven expertise in Snowflake — not surface-level familiarity.
• Strong SQL, data modeling (dimensional and semantic), and ETL/ELT pipeline design skills with a track record of production-grade implementations.
• Demonstrated experience building and managing semantic layers and metrics frameworks (e.g., dbt Semantic Layer, Tableau, Power BI).
• Experience with modern data stack tools and orchestration frameworks (e.g., dbt, Airflow, Prefect).

AI & Knowledge Architecture — Must-Have

• Genuine, hands-on experience architecting AI-first data platforms — candidates must be able to speak in depth to design decisions, trade-offs, and real-world outcomes.
• Solid understanding of Knowledge Architecture thinking: knowledge maps, multimodal data representation, vector databases, knowledge graphs, and context assembly.
• Practical experience integrating feedback loops, evaluation pipelines, and observability mechanisms around agentic AI and context retrieval systems.
• Experience with Vector DB technologies (e.g., Pinecone, Weaviate, pgvector) and Knowledge Graph tools — ability to select and implement the right tool for the use case.
• Familiarity with LLMs, RAG architectures, and Gen AI application development is strongly preferred.

What Will Set You Apart

• You can articulate not just what you built, but why — the architectural reasoning, constraints navigated, and the downstream impact on AI application performance.
• You understand that the success of AI applications is fundamentally dependent on the quality and structure of the underlying data — and you architect accordingly.
• You are as comfortable discussing embedding strategies and retrieval latency as you are optimizing Snowflake clustering keys.

EEO Statement

“At Latent View Analytics LLC, we value a diverse, inclusive workforce and we provide equal employment opportunities for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual’s race, color, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws. If you are unable to submit your application because of incompatible assistive technology or a disability, please contact us at . LatentView Analytics LLC will reasonably accommodate qualified individuals with disabilities to the extent required by applicable law.”