Top Data Jobs in London

Leads NTT DATA’s London Client Experience Centre briefing program, managing executive engagement strategy, agenda design, event execution, content quality, budgets, vendors, scheduling systems, and post-engagement reporting. The role advises account and sales teams, delivers C-suite experiences, coaches briefing staff, coordinates events and hospitality, tracks KPIs, and drives continuous improvement. It also supports executive communications, public relations, media relations, analyst relations, and senior stakeholder alignment.

Build and maintain scalable data pipelines, cloud data platforms, and business-ready data products. Develop data quality, testing, monitoring, and observability frameworks; contribute to architecture and technical design; and collaborate with analysts, data scientists, marketing, and engineering teams. The role also involves data modeling, ELT, workflow orchestration, CI/CD, infrastructure as code, stakeholder communication, and continuous improvement of analytics capabilities.

Leads JLL’s global data privacy program, setting strategy, ensuring regulatory compliance, advising senior stakeholders, managing privacy incidents and audits, and overseeing privacy and AI governance. The role drives automation and operational excellence, represents Ethics & Compliance with regulators and industry bodies, translates emerging regulations into business requirements, and develops a small team of privacy professionals.

Leads delivery of a major data centre campus project across design, tender, construction, testing, and commissioning. Manages clients, design teams, contractors, contracts, change, commercial matters, risk, reporting, and on-site delivery. Oversees 1–2 staff, supports proposals and business development, and ensures compliance with procurement, commercial, contractual, health and safety, security, and quality standards.

Deliver end-to-end data solutions using Microsoft Fabric, including lakehouses, pipelines, semantic models, and Power BI. Support Data Mesh, Data Product, governance, metadata, lineage, data quality, DataOps, deployment automation, monitoring, and operating model activities. Collaborate with business and technical stakeholders to develop scalable data products aligned with financial services regulations, GDPR, security, and auditability requirements.

Lead administration, maintenance, upgrades, security, configuration, integrations, reporting, and optimization of the Workday HCM and Financials platform. Provide user support, training, documentation, issue ownership, on-call assistance, and guidance on best-practice configuration. Coordinate cross-functional reviews, support feature adoption, identify system deficiencies, and help design new Workday solutions while mentoring team members.

Own end-to-end Microsoft Fabric and Azure data-platform delivery, including pipelines, data models, CI/CD, security, governance, monitoring, optimization, Power BI integration, documentation, production handover, and stakeholder collaboration. Mentor engineers and help clients modernize legacy or proof-of-value platforms into secure, scalable production services.

In-house Legal Counsel responsible for commercial contracting and data licensing across SaaS, technology, API, vendor, and procurement agreements. The role independently leads negotiations, advises on intellectual property, data rights, privacy, and commercial frameworks, and supports contract migrations, acquisitions, due diligence, templates, playbooks, and legal technology initiatives. The position works closely with Sales, Product, Data, Technology, and senior commercial stakeholders.

Develops end-to-end data solutions, including exploratory analysis, experimentation, predictive and prescriptive modeling, machine learning, and generative AI applications. Partners with business, product, and technical teams to solve complex insurance challenges, communicate insights, and support data-driven decisions. The role also involves validating AI-assisted work, applying statistical methods, and following engineering practices such as version control, testing, code review, and reproducible development.

Build and optimize scalable ETL/ELT pipelines, Spark jobs, Airflow DAGs, and dbt transformations. Design Lakehouse architectures using S3, Iceberg, and Delta Lake; develop reusable platform frameworks, CI/CD pipelines, data contracts, observability, and data quality controls. The role also supports streaming technologies, cloud data warehouses, dimensional modeling, schema evolution, and AI-enabled engineering improvements.

Leads European Revenue and Sales Operations, establishing governance, forecasting, pipeline management, performance frameworks, sales planning, incentive programs, and executive reporting. Partners with Sales, Finance, Growth, Operations, and country leadership to improve predictability, productivity, strategic deal execution, resource allocation, and profitable growth. Builds a coordinated regional operating model across geographically distributed teams and uses analytics, AI-enabled insights, and predictive models to guide business decisions.

Lead business development for DXC's insurance portfolio: originate and qualify pipeline, engage senior stakeholders, shape and close complex, high-value technology deals, collaborate with solution and delivery teams, and position AI-enabled and platform solutions to drive client transformation and expansion.

Manages virtual data rooms for client transactions and internal projects, including setup, permissions, document upload, indexing, quality checks, redaction, user support, reporting, closure, archiving, and audit logs. Ensures secure handling of confidential information and regulatory compliance while supporting legal teams, clients, and advisors. Contributes to improving data room processes and tools.

Lead and contribute to building scalable, production-grade data systems for private markets. Architect end-to-end data workflows supporting analytics and AI/ML, ensure observability, testing, and operational maturity, influence technical strategy, engage senior stakeholders, and manage and mentor software engineers while partnering with product, data science, and business teams.

Lead the design and development of a strategic, scalable data platform for high-volume trading data. Build data lakehouse, streaming, warehouse, metadata, lineage, and self-service capabilities using modern distributed technologies. Drive platform performance, reliability, security, regulatory compliance, cloud-native engineering, and infrastructure automation. Influence technology strategy across trading, data, and platform engineering while providing technical leadership and enabling teams to deliver data-intensive solutions efficiently.

Analyze and reconcile claims data from internal systems, TPAs, bordereaux, and data warehouses. Build SQL extracts, dashboards, KPI reports, reserve reviews, severity analyses, and executive reporting. Support underwriting and actuarial decisions through claims insights, loss development analysis, data quality investigations, and portfolio trends. Help develop claims rules, scoring models, exception reporting, and UAT test cases using SQL, Python, Azure Databricks, and related data platforms.

Work with ML and AI engineers to design and run experiments, evaluate and refine LLM-powered agent workflows, perform feature engineering and statistical analysis, prototype ML pipeline features, investigate production failure modes, and document reproducible findings that drive improvements in underwriting systems.

Design and evolve data architecture, platforms, models, integration patterns, and lifecycle practices supporting mission-critical Formula 1 applications. Collaborate with engineers, product owners, and stakeholders on architecture standards, modernization, technical debt reduction, tooling, CI/CD, and roadmap initiatives. Provide technical guidance, participate in engineering ceremonies, and support selected race weekends onsite at Biggin Hill and trackside.

Design, build, and maintain petabyte-scale batch and streaming data pipelines for robotics foundation model training and evaluation. Own data models, storage systems, ingestion, transformation, and orchestration infrastructure. Standardize processing across teleoperation and synthetic simulation datasets while operating production-grade distributed systems and infrastructure.

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.

Design and evaluate experiments, build statistical models, and generate evidence-based insights guiding sales and marketing investment. The role involves communicating findings to nontechnical colleagues, taking analytical workstreams from framing through decision-making, applying AI tools, and collaborating with data scientists, analysts, statisticians, marketing, sales, product, design, and engineering teams.

The Senior Data Analyst will design and analyze A/B tests, apply statistical methods, build Power BI semantic models and dashboards, and collaborate with data engineering teams on Databricks pipelines. The role includes data governance, quality monitoring, performance optimization, stakeholder presentations, and advising client teams. It is a client-facing hybrid position requiring three to four days weekly onsite in Central London.

Develop engagement forecasting models and ad hoc analyses for Disney+ across EMEA. Responsibilities include data collection, algorithm development, time-series forecasting, experimentation, causal impact analysis, semantic modeling, agentic AI solutions, stakeholder consultation, and communicating insights to technical and non-technical audiences.

Lead design and delivery of enterprise-scale data ingestion, orchestration, and provisioning solutions. Build trusted, governed data products and event-driven integration capabilities (APIs, Kafka), ensure data quality, observability, CI/CD, and operational resilience, and provide technical leadership across platform, application, and business teams to support analytics, regulatory reporting, ML/AI, and operational systems.

Deliver technical client deployments of the OneView platform, including data extraction, engineering, data science, cohort development, dashboards, predictive models, and analytics. Manage technical project delivery, stakeholder communication, benefits tracking, client training, testing, and platform improvements. Work with consulting, governance, product, and QA teams to translate client data into actionable insights and measurable social impact.