Top Data Jobs in London

Build and maintain automated cloud-based data solutions and scalable ETL pipelines. Design end-to-end data architectures, use Databricks, SQL, Python, and Spark, and ensure reliable, high-quality data delivery. Own complex backlog tasks, support code quality through reviews and pair programming, and experiment with innovative solutions. Help develop analytical tools that enable data-driven decisions, operational efficiency, and improved business performance.

Own data science at a media and entertainment startup by acquiring unconventional data, analyzing cultural and audience trends, building production ML models for virality and content analysis, designing experiments, and delivering trusted dashboards and insights that guide business decisions.

Maintains and develops enterprise data solutions across ingestion, integration, warehousing, modeling, pipelines, and business intelligence. The role monitors data quality and platform performance, supports reporting and analytics, resolves data and IT system issues, documents solutions, manages projects, and collaborates with stakeholders. It also involves security, compliance, troubleshooting, technology improvements, user training, occasional travel, and out-of-hours support.

Own the technical vision and architecture for a petabyte-scale data enrichment platform supporting machine-learning deployment, inference, evaluation, monitoring, annotation, and retraining. Build reliable backend services, APIs, workflows, and self-service capabilities across large GPU fleets. Lead cross-team architecture and delivery, establish platform metrics, improve reliability and cost efficiency, and mentor engineers.

Own the end-to-end lifecycle of proprietary private markets data across EMEA, including stakeholder outreach, data acquisition, validation, maintenance, quality improvement, and coverage growth. Build relationships with fund managers, investors, and clients; identify valuable data opportunities; apply analytical judgment and AI-enabled workflows; and contribute to strategic and revenue-generating initiatives while ensuring operational excellence and reliable execution.

Lead Preqin’s EMEA private markets data team, overseeing data acquisition, quality, coverage expansion, market engagement, and strategic execution. The role requires senior people leadership, commercial judgment, stakeholder relationship management, and deep private markets expertise. The leader will also drive data modernization and AI-enabled transformation, improve operational efficiency and scalability, and collaborate with Product, Technology, Commercial, and Data teams to deliver high-quality market intelligence to clients.

Graduate consultants analyze datasets, build dashboards and visualizations, support data pipelines and transformations, ensure data quality and governance, and communicate insights to stakeholders. The role involves client-facing, on-site work within transformation programs, supported by structured training, mentoring, and professional accreditations. Opportunities span data analysis, business intelligence, data engineering, governance, risk and compliance, and data science.

Lead design and implementation of secure, scalable cloud data architectures; drive adoption of AWS/Azure/GCP services; collaborate with data engineers to deliver data for analytics; assess data management technologies, promote data governance, mentor staff, and articulate architectural trade-offs to improve data quality and delivery supporting business roadmaps.

Lead the architecture and delivery of scalable batch and real-time data pipelines on GCP. Own data modeling, security, governance, reliability, performance, cost optimization, monitoring, and critical incident resolution. Translate complex business needs into robust data solutions, evaluate AI and data-engineering technologies, mentor engineers, and influence technical standards across teams.

Provide legal counsel on data acquisition and use for AI training, agentic systems, and search products. Advise product, research, engineering, safety, and commercial teams on intellectual property, privacy, licensing, AI regulation, security, and compliance. Build data governance standards, provenance controls, policies, review processes, and escalation pathways. Monitor U.S. and international legal developments, support strategic transactions, and translate evolving requirements into practical product and operational guidance.

Own data science initiatives across forecasting, classification, experimentation, recommendation, ranking, segmentation, and pricing. Develop and deploy predictive and econometric models for B2B buyer behavior and wholesale market dynamics. Design A/B tests, apply causal inference and regression techniques, collaborate with engineering on MLOps workflows, and communicate analytical recommendations and model limitations to Product, Finance, and Commercial stakeholders.

Own end-to-end data science and machine learning systems, including predictive modeling, forecasting, ranking, and LLM-powered features. Design RAG architectures, evaluate and deploy language models, and maintain reliable production pipelines through MLOps practices. Collaborate with Product, Engineering, and Commercial stakeholders to translate business challenges into measurable ML solutions, while communicating model limitations and trade-offs clearly.

Graduate data engineering role focused on building and maintaining SQL/dbt transformation pipelines, Python ingestion services, API data pipelines, and data quality processes. The role includes production coding, testing, debugging, code reviews, cloud infrastructure exposure, AI-assisted engineering, and collaboration with engineers, analysts, and product stakeholders. It is designed for recent graduates with foundational SQL and Python experience and offers mentorship, training, and cross-functional development opportunities.

Leads country-level People and Culture activities across the UK, Ireland, and South Africa. Provides senior stakeholder advice, employment-law expertise, employee-relations oversight, policy development, compliance management, compensation reporting, onboarding, performance management, training, engagement, and people metrics reporting. Partners with regional Employee Relations, Learning and Development, shared services, and analytics teams while promoting culturally sensitive, legally compliant, and high-performance workplace practices.

Design, build, operate, and evolve scalable, reliable, observable data lake and catalog platform services. Lead technical design, develop well-tested software, support SLOs and incident learning, collaborate with stakeholders, create implementation plans, mentor engineers, and contribute to platform architecture and open-source initiatives.

Analyze reader engagement and content performance to inform newsroom and business decisions. The role involves interpreting data within editorial and audience contexts, communicating insights to varied stakeholders, collaborating with Product and Engineering on experiments and features, and following privacy and data governance standards.

Partner with Product, Design, Engineering, and Analytics Engineering to analyze customer behaviour and product performance using funnel, cohort, and segmentation analyses; define and monitor KPIs; design and analyze A/B tests; build dashboards and reports; surface trends and recommendations; and apply AI/ML (including LLM-assisted) techniques to speed insight generation and measurement.

Leads end-to-end clinical data management for complex clinical trials, including data collection strategy, EDC requirements, data quality reviews, study documentation, vendor oversight, submission-ready datasets, audits, CAPA implementation, and continuous improvement. Mentors junior data management leads and represents Clinical Data Management on cross-functional and regulatory teams.

Build and maintain cloud software for a Customer Data Platform, delivering complex features from design through production. Lead technical delivery, design scalable solutions, improve code quality, testing, observability, reliability, and security. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and roadmap discussions, and mentor engineers.

Build, maintain, and release reliable cloud software for the Customer Data Platform. Lead complex feature delivery, make architectural decisions, improve code quality, testing, observability, security, and operational reliability. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and OKRs, and mentor engineers through pairing, reviews, and technical guidance.

Lead the strategy, engineering standards, governance, reliability, and modernization of the FinTRACE Databricks data platform. Build scalable data, analytics, AI, and agentic workloads for Finance and Risk Technology while partnering with senior stakeholders across trading and technology. Serve as the Databricks technical authority, improve platform security and operational excellence, manage transformation initiatives, and coach engineering teams.

Build and operate large-scale data ingestion, document processing, search, and serving systems for AI products. Responsibilities include handling massive heterogeneous datasets, designing schemas and indexes, enabling keyword, vector, and structured search, linking information across sources, and optimizing performance, reliability, latency, and cost. The role owns systems end to end, from source acquisition through user-facing functionality, and collaborates with AI researchers and product engineers.

Leads the origination, solutioning, shaping, and conversion of large-scale Data and AI transformation opportunities across Europe, the Middle East, and India. Owns a $30M+ pipeline of $3–5M+ deals, advises executive clients, develops value propositions and business cases, and connects data platforms, governance, and AI capabilities into executable transformation programs. Partners with account, architecture, practice, and delivery leaders to improve deal conversion, ensure delivery feasibility, expand engagements, and scale repeatable Data and AI offerings.

Analyze large datasets, apply statistical and machine learning models, perform data analytics, and create visualizations. Collaborate with founders and enterprise clients to solve complex business problems, embedding developed models into the Kepler AI platform. This early-stage role requires hands-on development, strong analytical communication, and comfort working in an ambiguous startup environment.

Designs, builds, and maintains AWS and on-premises infrastructure for machine learning engineers, data scientists, and software teams. Responsibilities include operating Kubernetes and MLOps platforms, responding to service requests, troubleshooting production issues, implementing infrastructure as code, improving reliability and developer experience, and supporting secure, scalable product delivery. The role requires cross-team collaboration, clear technical communication, and commuting to London two days per week.