Jobs at CUBE (cube.global)

Provides executive support to C-suite leaders by managing complex calendars, communications, meetings, travel, expenses, documentation, and confidential initiatives. Also oversees London front-of-house and office operations, including reception, visitors, meeting rooms, supplies, vendors, onboarding, events, and office presentation. Requires discretion, strong organization, polished communication, attention to detail, and the ability to manage competing priorities in a fast-paced technology environment.

Lead quality assurance initiatives for regulatory data products, ensuring high accuracy, SLA adherence, compliance, and consistent QA standards. Develop QA strategies, mentor team members, manage resources, report quality metrics, mitigate data risks, coordinate automated data quality checks, and improve documentation and processes across international teams.

The Data Quality Analyst profiles data, creates quality rules, monitors dashboards, investigates anomalies, identifies root causes, and performs controlled data corrections. The role improves SSIS/ETL ingestion pipelines, validates production data, documents governance and remediation procedures, and collaborates with technology, product, compliance, and business teams. It also supports AI-enabled monitoring and automation to improve data accuracy, reduce recurring defects, strengthen audit outcomes, and enhance customer-facing services.

Perform quality assurance reviews of regulatory data, meeting accuracy and SLA targets. Analyze datasets to identify errors, inconsistencies, and anomalies; collaborate with Data Quality and governance teams to implement controls, validations, and automated checks. Identify recurring issues, recommend process improvements, support automation initiatives, and improve QA documentation while ensuring compliance with data management policies and regulations.

Develop and maintain C#/.NET web scraping solutions that extract structured regulatory data from complex HTML websites. Troubleshoot dynamic, multilingual, access-restricted sites and improve caching, performance, automation, and reliability. Ensure extraction accuracy, document code and testing, manage tasks and deadlines, and collaborate with analysts, QA, and engineering teams. The role offers mentorship while progressively increasing responsibility for technical scraping and automation work.

Own the end-to-end production lifecycle of machine learning models and LLM integrations. Build Azure-based ML pipelines, deployment and serving infrastructure, monitoring, drift detection, rollback systems, experiment tracking, and model governance. Manage LLM providers, gateways, prompt versioning, observability, token usage, and costs. Partner with data scientists and engineers to establish MLOps practices, CI/CD, infrastructure as code, automated testing, reliability, and operational efficiency.

Converts complex PDF regulatory documents, including scanned and multilingual content, into accurate, accessible HTML. Responsibilities include handling tables, images, lists, footnotes, OCR issues, coding tasks in HTML, CSS, C#, .NET, and JavaScript, debugging, testing, quality assurance, documentation, and code reviews. The role also supports task management, collaboration with QA and project teams, and adoption of AI-driven tools to improve conversion efficiency and output quality.

Design and build scalable, production-grade agentic AI systems using LLMs to reason over data, orchestrate workflows, use tools, and drive decisions. The role combines backend engineering, data systems, NLP, prompt engineering, RAG, LLM evaluation, and agent orchestration. Responsibilities include translating AI research into enterprise SaaS products, deploying applications to production, experimenting rapidly, and collaborating with LLM engineers, software engineers, and data scientists in a hybrid Berlin AI lab.

Analyzes, cleanses, validates, and enhances customer risk and control data within the CUBE Network. Supports data matching using Python-based tools, collaborates with subject matter experts, and works with Product and Engineering to improve processing workflows. Helps define risk and control indices, automates analysis, and maintains high standards of data quality, consistency, and accuracy.

Serve as the single client-facing lead owning end-to-end implementation, stakeholder management, and BAU transition for RegTech SaaS clients. Run workshops, manage project delivery, collaborate with legal/finance/technology teams, perform UAT, and advocate client needs to influence product roadmap while ensuring service quality and retention.

Design and develop automated testing frameworks for backend services, APIs, microservices, and document-processing pipelines. Build validation for HTML, XML, JSON, PDF, schemas, metadata, data quality, and business rules. Create API, integration, regression, and end-to-end test suites using Python and modern frameworks. Integrate automated testing, reporting, and quality gates into CI/CD pipelines. Collaborate with Engineering, Product, DevOps, and Data Science teams, including applying AI/ML and LLM tools to test generation, content comparison, defect analysis, and root-cause identification.

Build and maintain scalable React + TypeScript frontend applications and AI agent interfaces, integrate with backend APIs and AI systems, implement component-based architecture, ensure performance, accessibility, testing, and collaborate within a small cross-functional product squad.

Design and own scalable, secure data architectures and pipelines. Translate business needs into technical designs, set standards, guide engineering teams, ensure performance, reliability, security, CI/CD, and maintain architectural documentation across global teams.

The Senior Customer Success Manager will enhance client relationships, drive product adoption, manage renewals, and ensure customer satisfaction for enterprise financial services clients.

Develop and maintain scalable Python backend services, RESTful APIs, and secure microservices. Design and optimize PostgreSQL databases, integrate cloud services and third-party APIs, implement authentication, write tested code, and support Docker-based CI/CD deployments. Collaborate with Product, QA, DevOps, and Architecture teams to improve application performance, reliability, scalability, observability, and operational excellence. The role owns end-to-end delivery of enterprise-grade backend components.

Own the architecture of an eight-stage Azure regulatory content pipeline, defining REST API contracts, event-driven integrations, engineering standards, and technical design processes. Mentor engineers across backend, monitoring, XML, and QA automation workstreams, while partnering with engineering leadership on technical risk and delivery sequencing. The role requires Python and FastAPI expertise, distributed systems design, Azure cloud experience, XML knowledge, performance engineering, observability, scalability, and fault-tolerant architecture.

Senior commercial leader responsible for winning new enterprise logos (£200K+ ACV), building a repeatable growth-segment sales motion, driving net revenue retention and expansion, maintaining accurate Salesforce pipeline and forecasts, and providing market intelligence to shape go-to-market and product decisions across APAC & Japan.

Own and scale CUBE's Americas mid-market: carry the segment number, build and coach a quota-carrying sales team, create onboarding and ramp programs, establish pipeline discipline, qualification standards and forecast rigor, and drive conversion improvements to make mid-market repeatable and predictable.

The Senior Software Engineer will design, develop, and maintain backend services and frontend applications while collaborating with other teams and mentoring junior engineers.

Lead commercial relationships for a book of enterprise financial-services accounts to protect recurring revenue. Drive renewals, monitor adoption data to identify risk, run quarterly executive business reviews, deliver training and workshops, map stakeholders, maintain Salesforce records, and surface customer feedback to Product, Sales, and Delivery to improve adoption and outcomes.