Knowledge & Data Architect

About the role

Location Qualifications: This role is available for hybrid work (2 days on-site) from our office in Bucharest.

About the position

Our data platform powers customer-facing embedded analytics that thousands of users rely on daily. But we're going further: Showpad is building a Revenue Intelligence engine that transforms raw signals — CRM data, email threads, call transcripts, and content engagement — into prescriptive AI guidance. You will be a foundational architect of the unified data intelligence layer that makes this engine possible.

You will participate in the design of our Medallion architecture, star schemas, and data governance frameworks — and critically, you will help define how structured and unstructured knowledge is modeled, connected, and retrieved. Your goal is to build a platform so robust and intuitive that other teams are empowered to build high-quality downstream models with speed and autonomy.

What You’ll Do

- Shape how we model and connect revenue data — accounts, deals, contacts, content, and engagement signals — across both structured and semantic layers

- Design and evolve the ontology that defines how entities relate, enabling relationship-aware reasoning beyond what traditional joins can offer

- Build and maintain a Knowledge Graph that supports multi-hop inference and entity disambiguation across our Revenue Intelligence engine

- Own our retrieval architecture — choose the right approach for each use case, whether that's RAG, vector similarity, graph traversal, or structured search

- Define how we ground LLM outputs in proprietary data, ensuring AI features are accurate, explainable, and production-ready

- Contribute to our Medallion architecture, star schemas, and data catalog, keeping the foundation solid as we scale

- Ensure our embedded analytics meet the demands of multi-tenant isolation, sub-second latency, and strict SLAs for white-labeled products

- Be a go-to resource for cross-functional teams — setting standards, reviewing models, and helping others build confidently on top of the platform

- Establish and maintain data contracts, schema governance, and lineage practices that keep data trustworthy at every layer

- Collaborate with Product and Engineering to turn complex business needs into clear, pragmatic technical roadmaps.

Our Tech Stack

Deep in the AWS ecosystem, with a growing AI/ML and semantic layer:

- 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 (e.g., OpenSearch kNN, pgvector), RAG pipelines, ontology frameworks (RDF/OWL or property graph models)