Job Opportunities API

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 —