Top Tech Jobs & Startup Jobs

As a Senior Sales Specialist, you'll drive revenue by selling analytics solutions to financial institutions, collaborate with teams, and manage sales strategies.

Manage and grow existing accounts by building deep client relationships, performing solution selling and prospecting, advancing opportunities through sales stages, completing sales administration (proposals, calculators, Salesforce), mentoring peers, and consistently meeting or exceeding sales targets.

Own the vision, roadmap, backlog, and delivery of secure content sourcing platforms for financial data. Partner with engineering, data, operations, and business stakeholders to define capabilities, prioritize features, lead PI planning, coordinate Agile Release Trains, and drive adoption and measurable business value. The role requires strong product ownership, data platform knowledge, financial services experience, Agile expertise, and the ability to communicate priorities and trade-offs across technical and executive audiences.

Leads software engineering teams developing scalable AI solutions for financial markets. Responsibilities include setting technical strategy, overseeing architecture through deployment, hiring and mentoring engineers, collaborating on product and data science roadmaps, and ensuring reliable delivery through cloud, DevOps, testing, and monitoring practices. The role requires extensive software engineering leadership, strong Python or Java expertise, and experience delivering high-performance systems.

Supports project managers and portfolio leadership across a £35 million change portfolio. Maintains departmental data, creates status dashboards and insights, tracks deliverables against timelines and budgets, monitors governance and change roadmaps, and produces project reporting for senior stakeholders. The role also provides administrative support and an opportunity to develop project management expertise.

Manages the strategy, roadmap, delivery, governance, quality, and modernization of end-of-day pricing and time-series data assets. Partners with Engineering, Product, Content Operations, contributors, exchanges, and vendors to expand coverage, improve data integrity, support platform transformation, and meet client and business requirements. Provides subject matter expertise on historical pricing and market data while supporting documentation, training, communications, and stakeholder engagement.

Monitors, validates, and maintains real-time, reference, and historical pricing and metadata for global FI/FX and money-market content. Investigates market events, resolves client pricing queries, identifies data trends and issues, supports senior analysts on projects, and leads process improvements. The role requires rotational shifts to provide 24/5 coverage across Asian, European, and North American markets.

Develops data-processing solutions, quantitative models, machine-learning applications, and automated database pipelines for legal entity content. Analyzes patterns and trends, builds proofs of concept, visualizations, and analytics tools, and supports deployment with development teams. The role collaborates with business, content, product, and data strategy stakeholders while communicating findings to technical and non-technical audiences.

Designs and owns enterprise integrations, REST and GraphQL APIs, event-driven services, microservices, and cloud-native applications. Builds full-stack applications with React and TypeScript, backend services with Node.js/NestJS or Java/Spring Boot, and integrations using AWS messaging and workflow services. Responsibilities include database engineering, deployments, production support, API security, testing, automation, code reviews, integration standards, stakeholder collaboration, mentoring, and technical leadership.

Lead the architecture, development, deployment, monitoring, governance, and continuous improvement of production-scale machine learning platforms. Establish MLOps standards, feature stores, data quality frameworks, testing strategies, explainability practices, and observability. Build low-latency inference services, oversee AWS SageMaker workflows, guide validation and experimentation, solve reliability and scaling challenges, and coach engineers while driving technical direction.