Knowledge & Data Architect

About the role

Showpad is building a Revenue Intelligence engine to convert raw inputs like CRM data, emails, and call transcripts into AI-driven guidance. You will design the foundational data intelligence layer, focusing on how knowledge is modeled, retrieved, and connected to support our analytics platform.

Key facts

What you'll do

• Design models for revenue data including deals, contacts, and engagement signals across semantic and structured layers.
• Develop and evolve ontologies to enable relationship-aware reasoning beyond standard database joins.
• Maintain a Knowledge Graph to support entity disambiguation and multi-hop inference.
• Architect retrieval systems, selecting between RAG, vector similarity, structured search, or graph traversal.
• Ground LLM outputs in proprietary data to ensure accuracy and explainability.
• Contribute to Medallion architecture, star schemas, and data catalogs to maintain data integrity.
• Support embedded analytics with a focus on multi-tenant isolation and sub-second latency.
• Establish data contracts, lineage practices, and schema governance.
• Collaborate with engineering and product teams to translate business requirements into technical roadmaps.

Requirements

• Expertise in data modeling, specifically Kimball and dimensional design.
• Experience managing both relational and semantic models, including ontology design.
• Practical knowledge of RAG pipelines, including embedding design, chunking, and retrieval evaluation.
• Background in event-driven systems handling unstructured data such as documents and transcripts.
• Proficiency in DevOps, CI/CD, and DataOps practices.
• Ability to communicate technical trade-offs to stakeholders across engineering and product teams.

Skills & tools

• Orchestration & Transformation: dbt, Glue Jobs, AWS ECS Fargate
• Storage & Catalog: S3 Data Lake (Iceberg/Hive), Glue Data Catalog, Lake Formation
• Compute & APIs: AWS Lambda (TypeScript/Python), Athena
• Analytics & BI: ClickHouse, OpenSearch, AWS QuickSight, Luzmo
• Knowledge & Retrieval: Amazon Neptune (or equivalent graph DB), vector stores (OpenSearch kNN, pgvector), RAG pipelines, ontology frameworks (RDF/OWL or property graph models)
• Infrastructure: CDK, CloudFormation

Practical notes

Showpad provides company-wide days off, volunteer time off, and professional development opportunities. We are an equal opportunity employer and do not discriminate based on identity, background, or protected characteristics.