• Identify and suggest new technology to enhance data systems and support business needs • Collaborate with business analysts, Data Management teams, Data Analytics, Data Science, and other IS analysts to convert business requirements into enterprise data solutions • Design and build enterprise data storage and access solutions, integrating multiple data sources and systems • Maintain data quality, integrity, and reliability by promoting best practices • Develop formal architectural documentation based on project needs • Recommend standards to optimize performance and efficiency within the data storage ecosystem • Implement security frameworks to manage data and visualization access effectively • Build consensus between business and technical partners and understand other business units involved • Handle highly complex requests and solutions spanning IS and business functions • Design, execute, modify, and optimize data masking for integrated data sources • Lead, document, and implement aspects of information provisioning, data analysis, profiling, modeling, masking, provisioning, and R&D • Manage current and future needs concerning data design, structure, content, and inventory • Analyze, design, and support implementation for complex service requests • Research business issues and aid in developing complex data models • Conduct unit testing, including creating test cases based on requirements; possess advanced knowledge of unit and user acceptance testing • Review test plans and participate in testing to ensure deliverables meet expectations • Actively promote teamwork and continuous improvement • Practice active listening with colleagues • Communicate promptly and escalate issues to leaders or project managers as needed • Write clear, professional, and engaging business correspondence • Encourage peer review of deliverables, supporting consistent and effective documentation that aligns with team standards and templates • Coach and mentor others in the data management teams

• Design and implement reliable, scalable data ingestion and integration pipelines for structured, semi-structured, unstructured, and multi-modal data (e.g., databases, documents, APIs, events), ensuring data is AI-ready, governed, secure, and observable. • Build and scale retrieval infrastructure, including vector storage, embedding pipelines, hybrid search, and graph-based knowledge representations, while optimizing data modeling for retrieval quality. • Develop and operate agent memory systems and pipelines for AI system signals (tracing, feedback, evaluation, and usage data) to support observability and continuous improvement. • Apply data quality, validation, monitoring, and testing frameworks in production pipelines, ensuring governance, access control, lineage, and security standards are met, including safe handling of sensitive data in AI retrieval and generation workflows. • Monitor, troubleshoot, and optimize AI data pipelines and retrieval workflows for reliability, performance, and cost, with strong observability and resilient processing patterns. • Design and support evaluation workflows for AI systems, enabling offline testing, benchmarking, and continuous improvement of retrieval and agent performance over time. • Lead pragmatic platform evolution by defining clear contracts between AI services and data systems, reducing coupling, and improving developer experience.

Role Description There's a new archetype taking shape in finance: someone who pairs real finance judgment with the ability to build their own solutions — wiring AI agents into the GL, standing up data pipelines without filing an engineering ticket, automating workflows end to end instead of running them by hand. VulnCheck's finance function is scaling across three entities (US, UK, Singapore), and we need someone who builds the systems and AI-driven workflows that keep Finance accurate and fast as we outgrow manual processes. - AI-driven close & reporting workflows. - Build Claude-powered workflows that draft variance commentary, flag anomalies, and assemble reporting packages. - Financial systems & pipelines. - Connect our GL, billing, CRM, and spend management systems into a single source of truth for ARR, cash, and unit economics, through API/MCP connections you own rather than tickets you file. - Model infrastructure. - Own the technical architecture behind the financial model, including version control, forecast wiring, and scenario logic, so it stops needing a manual rebuild every quarter. - Dashboards & reporting automation. - Build self-serve dashboards for ARR roll-forward, cohort retention, burn multiple, and deferred revenue that are live and drillable instead of rebuilt by hand each cycle. - Close automation. - Take manual steps out of the monthly and quarterly close through scripting, AI agents, and system integration. - Audit & controls support. - Build documented, repeatable data workflows that hold up under external audit scrutiny (ASC 606, ASC 830, transfer pricing). Automated doesn't mean uncontrolled. - Cross-functional data requests. - Be the technical bridge between Finance and GTM, RevOps, and Engineering when a question just needs someone to query the data instead of waiting on someone else to pull it. Qualifications - LLM fluency: real, daily use of Claude (or similar) to analyze data, draft narratives, and check outputs against source documents. - Workflow automation: you've actually found a manual, recurring process and killed it, not just wanted to. - Systems integration: you understand how data flows through a GL/ERP, know basic SQL and API principles, and can connect two systems yourself without opening an engineering ticket. - Data literacy: you can read a GL, understand revenue recognition and ARR/cohort concepts, and can tell when a dataset is incomplete or wrong before it reaches the board. Requirements - Demonstrated hands-on use of AI tools (Claude, ChatGPT, Copilot, or similar) in a real finance or accounting workflow. Active daily use, not just familiarity. - Working SQL skills. Comfortable querying and modeling real datasets. - Core finance/accounting fluency: GL literacy, revenue recognition basics, an FP&A or accounting background. - Evidence of having automated or systematized a manual process end to end. Preferred Qualifications - Experience connecting AI tools to live systems via API or MCP (Model Context Protocol). - Exposure to a BI or semantic layer tool (Looker, dbt, or similar). - Prior FP&A, accounting, or RevOps role at a B2B SaaS company. - Experience operating through an external audit cycle. - Exposure to multi-entity or multi-currency finance operations. Benefits - Unlimited PTO - 401k plan with company match - Comprehensive healthcare coverage - Generous paid parental leave - Remote friendly environment with flexibility - Expense reimbursement for Cell Phone & Internet - Ongoing professional development, coaching, and learning resources - Opportunities for career advancement within a fast-growing team

Role Description Du analysierst gemeinsam mit deinem Team die fachlichen und technischen Anforderungen der Kunden und setzt diese in Data & Analytics Lösungen mit SAP BW (BW/4 HANA) und SAP Datasphere um. Dabei tauchst du in deren Geschäftsprozesse ein und lieferst das entsprechende Datenmanagement. - Schwerpunkt im Backend: Konzeption und Implementierung von Datenmodellen und ETL-Strecken. - Beteiligung am Test und an der Übergabe in den Echtbetrieb beim Kunden. - Ergänzende Erfahrungen mit Frontend-Tools, wie SAP Analysis for Office und der SAP Analytics Cloud, sind gerne gesehen. - Übernahme der Verantwortung für das Projekt und das Windhoff Projektteam in der Funktion des Senior Consultants. - Wissenstransfer durch Schulungen, Projektpräsentationen und interne/externe Technologie-Events. Qualifications - (Mehrjährige) Projekterfahrung im Data Warehouse Umfeld, insbesondere mit SAP BW und SAP Datasphere. - Beherrschung der Modellierung mit LSA / LSA++ sowie gängige Programmierung in ABAP, AMDP, CDS und SQL-Script. - Hohe Eigeninitiative und sehr gute Kommunikationsfähigkeit. - Fließende Deutsch- und gute Englischkenntnisse. - Begeisterung für neueste Technologien wie SAP Business Data Cloud und Databricks. Benefits - Standortunabhängige Anstellung: bis zu 90 % Homeoffice möglich. - 130 Data & Analytics Consultants zum Austausch sowie eine interne Weiterbildungsacademy. - Langfristige Projekte mit wechselnden technologischen Herausforderungen. - Festes Budget für Weiterbildungsmöglichkeiten sowie persönliche und fachliche Entwicklungsperspektiven. - Gute Work-Life-Balance durch flexible Arbeitszeiten, Überstundenausgleich sowie bis zu 40 Tagen Urlaub. - Erstklassige Hard- und Softwareausstattung sowie Leasingangebote für Firmenwagen, IT-Hardware und Fahrräder. - Kurze Wege im Unternehmen, fairer Umgang miteinander sowie unterhaltsame Teamevents.