Knowledge Architecture

Design knowledge semantic clusters, Taxonomies, and graph structures using RDF/OWL and SPARQL, integrating business context and permission-aware retrieval.

Advise on Lakehouse and warehouse architectures across Snowflake, Databricks, BigQuery, Redshift, and Synapse, using Delta Lake and Apache Iceberg; apply Data Mesh and Data Fabric patterns where appropriate.

Define integrations for SAP, Salesforce, AS/400, Dynamics 365, and Microsoft 365 across AWS, Azure, and Google Cloud Platform. Deliver Change Data Capture (CDC) and Streaming Ingestion through Debezium, Fivetran, Kafka, or native capture.

Establish Master Data Management and Entity Resolution across fragmented sources, with validation, lineage, ownership, and lifecycle controls.

Build document processing, Embedding, vector indexing, and retrieval foundations for LLM applications; design feature stores using Feast, Tecton, Databricks Feature Store, or SageMaker with online/offline consistency and point-in-time correctness.

Implement governed catalogs through Unity Catalog, Microsoft Purview, Collibra, Alation, DataHub, or Atlan; establish access controls, sensitive-data protection, and retention standards.

Deliver reliable natural-language-to-SQL through schema grounding, metric definitions, query guardrails, and evaluation; develop prototypes, reusable reference architectures, and actionable platform feedback.

Deep expertise in Ontology, Semantic Layer, and Data Modeling design, translating fragmented enterprise information into shared business meaning.

Strong architectural judgment, executive communication, and experience reviewing agent-generated models and pipelines rather than treating automation as a black box.

A collaborative approach to mentoring architects, resolving technical escalations, and translating field experience into product improvements., Experience delivering large-scale enterprise data systems and leading architecture across concurrent, complex customer engagements.

Expertise in relational and NoSQL modeling, dimensional and semantic design, and ETL/ELT pipelines; hands-on SQL, Python, and distributed processing with Spark.

Demonstrated ontology, knowledge graph, enterprise integration, governance, and agent-assisted delivery experience, including reviewing and correcting generated outputs.

Customer-facing consulting or engineering experience and a relevant bachelor’s or master’s degree, or equivalent practical experience.

Preferred Qualifications

Production GenAI and RAG foundations, vector databases, retrieval evaluation, and enterprise text-to-SQL delivery.

Graph database experience with Neo4j, Amazon Neptune, or TigerGraph.

Exposure to LangChain, LangGraph, CrewAI, or the Model Context Protocol, plus regulated-industry data security practices.

Thinking Minds’ client offers incentive opportunities, stock options, health benefits, retirement matching, paid leave, and opportunities to influence enterprise AI architecture.