Top Data Jobs in London

Drive enterprise sales for Quadient by executing territory plans, prospecting and building pipeline via direct and partner channels, presenting value-based proposals, responding to RFPs, using Salesforce for forecasting, engaging internal teams, negotiating contracts, and positioning Quadient Professional Services and product solutions (CCM/CXM, SaaS/Cloud) to meet quota.

The Senior Data Strategy Consultant will manage client data strategies, facilitate data integration, and improve data processing efficiency across 80+ clients, providing training and ensuring best practices.

As Senior Product Manager, you'll manage the product roadmap, engaging with healthcare professionals and driving cross-functional execution in a high-growth tech environment.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Manage a named portfolio of UK telecommunications accounts, drive net-new and expansion revenue, independently build pipeline through prospecting, develop value-based AI and platform business cases, and collaborate cross-functionally to close large B2B deals.

Lead WAM data architecture and cloud transformation engagements: define enterprise data strategies, design canonical WAM data models, architect cloud-native data platforms, integrate core WAM systems, establish data quality and control frameworks, and advise senior stakeholders on technical-to-business translation.

Design, build, and operate a high-throughput, low-latency real-time data platform using Flink and Kafka. Develop Java and .NET processing and APIs, manage cloud infrastructure (AWS, Terraform), implement CI/CD (Harness), and ensure observability, scalability, performance, and cost-efficiency across production systems.

Lead a squad to design, build, and operate the firmwide data catalogue (Fusion). Provide technical leadership, drive platform enhancements, enforce secure AI-assisted development practices, promote automation and governance, and collaborate with stakeholders to deliver scalable, reliable data platform solutions while mentoring engineers and improving operational stability.

Lead product vision, roadmap, and delivery of CCOR data and analytics capabilities. Partner with Compliance, First Line, Technology, Data Science and stakeholders to translate requirements into features, manage Agile backlog, oversee UAT, training and adoption, and ensure regulatory alignment. Drive analytics and AI/LLM innovation to improve decisioning, controls, and operational risk outcomes.

Lead sales of J.P. Morgan's data, research content, and analytics solutions to buy-side clients. Own full sales cycle from prospecting and pipeline generation to deal execution and relationship management. Partner with product, content, and internal stakeholders to shape go-to-market strategies, run client workshops, capture Voice of Client feedback, and support product roadmaps. Provide high-touch coverage for institutional clients and coordinate subject matter experts to deliver tailored engagements.

Lead design and delivery of scalable GenAI platforms and services: build RAG pipelines and agents, model/version management, monitoring and drift mitigation, cloud-native microservices and deployment, testing and guardrails to reduce hallucinations, ensure compliance, and mentor engineering teams.

Lead design, build, and maintain large-scale, secure data engineering solutions. Produce and review production code, improve operational stability, drive AI-assisted engineering adoption, evaluate vendors, and shape technical strategy and team culture.

Lead reliability, performance, and automation of analytics pipelines and reporting. Define SLIs/SLOs, monitor data quality/lineage, enable self‑service dashboards, enforce data risk/controls, drive FinOps/capacity insights, lead stakeholder reviews, and mentor junior engineers.

Lead design and delivery of cloud-native data and AIML production systems. Build data engineering, backend, Cloud infra/DevOps and MLOps stacks, produce secure production code, architect microservices and distributed, data-intensive applications, and drive adoption/governance of AI-assisted engineering practices while communicating with stakeholders.

Own end-to-end Party and Account data products: define APIs and schemas, manage ingestion, validation, persistence, batch and GraphQL distribution, profile and resolve data issues, set KPIs and SLAs, onboard producers, and use AI-assisted tools to accelerate SDLC while ensuring PII, access controls, and auditability.

Lead product strategy and roadmap to re-imagine the advisor/client trading experience. Define requirements, partner with design and technology, manage delivery across scrum teams, pilot and roll out capabilities, and use user research and metrics to drive prioritized features while maintaining stakeholder relationships and control requirements.

Lead and enhance the data control environment for International Consumer Business, overseeing risk identification, remediation, governance, and regulatory/audit engagements. Advise on AI/ML and model risk, drive strategic transformation, manage projects, ensure compliance with operational risk standards, and communicate with senior leaders to foster continuous improvement.

Lead index and benchmark data management across the product lifecycle: own data quality, governance, cataloging, documentation, and risk. Partner with product, technology, and business teams to drive data-driven product innovation, ensure compliance, and promote data literacy while leveraging AI and cloud platforms.

Lead design and delivery of high-performance, real-time data pipelines and analytics for trading. Drive AI-enabled data engineering, mentor engineers, manage global initiatives, and improve SDLC, testing, and operational reliability.

Lead design, delivery and maintenance of on-prem and cloud market-data distribution systems (real-time, historical, AI/ML use cases). Collaborate with product/stakeholders, provide 3rd-level operational support, validate AI-assisted recommendations, ensure resiliency, security, auditability and strong process controls.

Lead design and deliver cloud-native, production-scale data and AI/ML engineering solutions. Build data engineering, backend, DevOps and MLOps stacks; produce secure production code and architecture artifacts; troubleshoot complex systems; collaborate with stakeholders and engineering communities to industrialize AI/ML models.

Design, build, test, and maintain scalable ELT data pipelines and architectures using Python and Databricks. Ensure data security, entitlement controls, and SDLC quality (CI/CD, testing). Use SQL/NoSQL, update data models, apply enterprise-authorized AI responsibly, and support controls and auditability across the data lifecycle.

Design, develop, and refine end-to-end data models and prototypes for financial planning and analysis. Support cloud data lakehouse solutions, ETL/ELT pipelines, dashboard/cube consumption, and help embed AI/LLM tools into workflows while collaborating with finance, analytics, and technology teams.

Lead a technical area driving design, development, and delivery of AI/ML and data platform solutions. Own architecture, governance, and adoption of AI-assisted engineering practices, ensure operational stability, and influence stakeholders while hiring and developing engineering talent.

Design, build, and maintain large-scale, secure data engineering solutions. Lead architecture, deliver production code, drive automation and operational stability, mentor teams, and evaluate vendor/technology choices to advance risk technology platforms.