# Knowledge & Data Architect at Showpad

> Romania · Staff · Software engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Ashowpad%3A7858216 (read 2026-11-12).

## Facts (observed)

- Company: [Showpad](https://alderhq.app/companies/showpad)
- Location text: Bucharest; Bucharest
- Where it can be done from (inferred): Romania (evidence: "Location: Bucharest; Bucharest")
- Posted: 2026-04-28 (199 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-07
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/showpad/jobs/7858216
- Skills listed (inferred): TypeScript, Python, Elasticsearch, AWS, dbt, LLMs

## Pay and sponsorship

- Posted pay: not posted
- Licensed UK sponsor: registered as "Showpad Ltd" (checked 2026-11-09, [register](https://www.gov.uk/government/publications/register-of-licensed-sponsors-workers))

## Description (the employer's text)

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)
- Infrastructure: CDK / CloudFormation.
### What We’re Looking For

- You think in systems, not just solutions — you design for the problem two steps ahead, not just the one in front of you
- Strong foundation in data modeling: you know Kimball and dimensional modeling well, and you know when to bend the rules
- Comfortable working across both relational and semantic models — you understand ontology design and when a knowledge graph unlocks what a schema can't
- Hands-on experience with RAG pipelines: chunking strategies, embedding design, retrieval evaluation, and what makes them fail in production
- You've worked with large-scale, event-driven systems and know how to handle messy, unstructured inputs like transcripts, emails, and documents
- DevOps is second nature — CI/CD, monitoring, and DataOps practices are how you work, not things you bolt on later
- You can talk trade-offs clearly: a PM, an engineer, and a business stakeholder should all leave the same conversation understanding your recommendation
- You're pragmatic about tooling — you'll reach for the right retrieval strategy, not the most impressive one.
### Company Highlights

Welcome to the new era of revenue effectiveness. The merger of Showpad and Bigtincan is creating the first AI-powered platform to strengthen the entire field selling motion.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Ashowpad%3A7858216 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.