{"id":155161,"url":"https://alion.io/job/showpad-knowledge-data-architect","title":"Knowledge & Data Architect","company":{"id":54384,"name":"Showpad","domain":"showpad.com","url":"https://alion.io/company/showpad","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":95,"open_postings":3,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":92,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bucharest, Romania"],"countries":["RO"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":46000,"max_usd":111000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":691},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Neptune","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"GraphDB","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"RAG","optional":false},{"name":"Amazon ECS","optional":true},{"name":"Amazon S3","optional":true},{"name":"AWS","optional":true},{"name":"AWS Fargate","optional":true},{"name":"AWS Glue","optional":true},{"name":"AWS Lambda","optional":true},{"name":"ClickHouse","optional":true},{"name":"dbt","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Python","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-04-28T07:04:38Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-16T22:05:22Z","board_verified":true,"closed_at":null,"days_open":171,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":170},"description":"Location Qualifications: This role is available for hybrid work (2 days on-site) from our office in Bucharest.\nAbout the position\nOur 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.\nYou 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.\nWhat You’ll Do\nShape how we model and connect revenue data - accounts, deals, contacts, content, and engagement signals - across both structured and semantic layers\nDesign and evolve the ontology that defines how entities relate, enabling relationship-aware reasoning beyond what traditional joins can offer\nBuild and maintain a Knowledge Graph that supports multi-hop inference and entity disambiguation across our Revenue Intelligence engine\nOwn our retrieval architecture - choose the right approach for each use case, whether that's RAG, vector similarity, graph traversal, or structured search\nDefine how we ground LLM outputs in proprietary data, ensuring AI features are accurate, explainable, and production-ready\nContribute to our Medallion architecture, star schemas, and data catalog, keeping the foundation solid as we scale\nEnsure our embedded analytics meet the demands of multi-tenant isolation, sub-second latency, and strict SLAs for white-labeled products\nBe a go-to resource for cross-functional teams - setting standards, reviewing models, and helping others build confidently on top of the platform\nEstablish and maintain data contracts, schema governance, and lineage practices that keep data trustworthy at every layer\nCollaborate with Product and Engineering to turn complex business needs into clear, pragmatic technical roadmaps.\nOur Tech Stack\nDeep in the AWS ecosystem, with a growing AI/ML and semantic layer:\nOrchestration & Transformation: dbt, Glue Jobs, AWS ECS Fargate\n Storage & Catalog: S3 Data Lake (Iceberg/Hive), Glue Data Catalog, Lake Formation\n Compute & APIs: AWS Lambda (TypeScript/Python), Athena\nAnalytics & BI: ClickHouse, OpenSearch, AWS QuickSight, Luzmo\n 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)\nInfrastructure: CDK / CloudFormation.\n What We’re Looking For\nYou think in systems, not just solutions - you design for the problem two steps ahead, not just the one in front of you\nStrong foundation in data modeling: you know Kimball and dimensional modeling well, and you know when to bend the rules\nComfortable working across both relational and semantic models - you understand ontology design and when a knowledge graph unlocks what a schema can't\nHands-on experience with RAG pipelines: chunking strategies, embedding design, retrieval evaluation, and what makes them fail in production\nYou've worked with large-scale, event-driven systems and know how to handle messy, unstructured inputs like transcripts, emails, and documents\nDevOps is second nature - CI/CD, monitoring, and DataOps practices are how you work, not things you bolt on later\nYou can talk trade-offs clearly: a PM, an engineer, and a business stakeholder should all leave the same conversation understanding your recommendation\nYou're pragmatic about tooling - you'll reach for the right retrieval strategy, not the most impressive one.\nCompany Highlights: \nWelcome 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.\nBy unifying two industry-acclaimed solutions for content, readiness, engagement, and intelligence into a single operating system, we are delivering unrivaled scale and accelerated growth for our 2,000+ combined customers worldwide, including leaders in manufacturing (Dow, Dupont), healthcare (Fujifilm, Kaiser Permanente, Johnson&Johnson), CPG (Smucker’s, Mitsubishi, Stanley Black & Decker), and enterprise tech (HID, CultureAmp, LastPass).\nAcclaimed by analysts and adored by customers, we’re recognized as a Leader in the Forrester Wave™ Revenue Enablement Platforms, honored by Gartner as a Customers’ Choice in Revenue Enablement Platforms, and, together, endorsed by nearly 3,000 5-star customer reviews on G2.\nUnited, we’re focused on powering the next generation of field selling success through a more holistic engine that creates lasting value for our customers and a new vision for our category.\nDiscover the revenue team outcomes we’re driving together at showpad.com.\nWhat you can expect from Showpad\nWe welcome every voice and are committed to building a truly inclusive environment where your differences are not just welcomed, they are celebrated. We’re building a best-in-class experience for our employees and are always identifying opportunities to encourage our team to be their authentic selves. Whether it’s additional company-wide days off, paid time off to volunteer at non-profit organisations, personal development opportunities or professional stretch assignments, you can expect Showpad to support you. \nWe are committed to creating a diverse and inclusive organisation and are proud to be an equal opportunity employer. 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