Top Data Jobs in London

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.

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.

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.

The Global Data Strategy Lead will oversee the development and execution of data strategies for WPP Media's largest clients, focusing on driving growth through data application, innovation, and technology integration. This role requires leadership in creating effective data solutions and building relationships with stakeholders to ensure competitive advantage.

The Managing Consultant will lead data science initiatives, drive business growth in financial services, and build client relationships while managing teams and delivering high-impact solutions.

Drive the go-to-market strategy for EMEA Financial Services, focusing on Banking, Capital Markets, and Insurance. Develop relationships, execute growth plans, and engage in thought leadership.

Principal Data & AI Consultant responsible for advising clients on enterprise data and AI strategy, architecture, and transformation. The role involves designing batch and streaming data pipelines, enabling analytics and AI/ML applications, modernizing cloud data platforms, and championing governance, security, privacy, and compliance. The consultant will shape technical roadmaps and proposals, guide executive stakeholders, build client relationships, and mentor colleagues while contributing to Waracle’s technical direction.

Leads Data and AI strategy engagements for clients, managing teams, deliverables, stakeholder relationships, workshops, business cases, operating models, and strategic recommendations. Develops board papers, AI and GenAI roadmaps, data products, thought leadership, and statements of work. Requires strong analytical, communication, commercial, and leadership capabilities, plus familiarity with cloud platforms, data visualization, statistical analysis, and strategic frameworks. The role involves client-site work and collaboration with global AI teams.

Partner with Marketing to own attribution, ROI, conversion, and funnel analysis. Design experiments, diagnose anomalies, optimize spend, and translate data into clear recommendations. Collaborate with Data and Analytics Engineers to shape data models and pipelines, and communicate insights to non-technical stakeholders.

Lead infrastructure engineering for critical market data platforms: provide production support, implement changes, lead architecture and automation workstreams, validate AI-assisted recommendations, ensure performance, resilience and data quality, and collaborate across platforms to modernize and stabilize trading market data infrastructure.

Supports international insurance and risk management for data center development and operations. Responsibilities include insurance placements and renewals, construction and property coverage administration, claims coordination, contract and financing diligence, compliance tracking, stakeholder coordination, risk analysis, reporting, and transitions from construction insurance to operational coverage.

Build and operate high-volume ingestion pipelines for procurement and supply-chain data. Design schemas, catalogs, warehouses, lakehouses, and feature stores; establish data quality, monitoring, observability, governance, and lineage standards. Collaborate with AI, Platform, and Product teams to provision reliable datasets for analytics and machine learning. Own end-to-end data solutions, CI/CD, integrations, and architecture supporting enterprise customers.

Analyze and improve international storefront growth through product, customer, and operational analytics. Responsibilities include building KPI frameworks, dashboards, and automated reporting; conducting deep-dive analyses; designing and evaluating A/B tests; applying statistical and causal methods; forecasting trends; and partnering with product, engineering, merchandising, and business teams to guide prioritization and expansion. The role also supports data quality, governance, instrumentation, and analytics best practices.

Design and maintain scalable data pipelines and reusable platform tooling using Python, Scala, Spark, Azure, Databricks, and Delta Lake. Establish data engineering standards, CI/CD, data quality, testing, observability, metadata management, and performance optimization. Partner with data scientists, analysts, engineers, platform teams, and security stakeholders to deliver secure, reliable, cost-efficient, production-ready datasets across business domains.

Hands-on role responsible for preparing and governing business data, developing dashboards and actionable insights, enabling AI agents and RAG capabilities, evaluating agent performance, and identifying commercial and operational opportunities. The position partners with technical and business teams across Salesforce, event, marketing, and operational systems to improve data quality, build reusable intelligence, and measure the value of AI and automation.

Develops and maintains centralized financial datasets supporting management P&L, balance sheet substantiation, statutory reporting, and AI-enabled finance tools. Builds reconciliations to the general ledger, partners with data engineering and IT teams, leads UAT, documents datasets and tools, trains Finance users, and supports migration to IT-owned platforms. The role requires strong regulated-finance experience, advanced Excel skills, control expertise, and familiarity with financial products and data-manipulation tools.

Own data quality, governance, reporting, and intelligence across CRM, marketing, and client relationship platforms. Develop pipeline, cross-sell, and client growth insights; establish data stewardship models; lead AI-enabled enrichment and predictive data-quality initiatives; and translate complex analysis into actionable recommendations for senior stakeholders and growth teams.

Own Spiko’s AI and data strategy as a senior engineer. Build production AI features, including agents, RAG, tool use, MCP integrations, and AI-assisted workflows across the company. Lead AI engineering practices and infrastructure while owning the cloud data platform built on BigQuery, Datastream, and Metabase. Partner with technical and non-technical stakeholders and help grow the AI and data team.

Own the enterprise data architecture and technical roadmap for a modern Databricks lakehouse platform. Design medallion architecture, data models, secure integrations, privacy-preserving capabilities, governance, lineage, observability, and AI-ready pipelines. Lead prototypes, technical reviews, optimization, and complex issue resolution while partnering with engineering, product, security, AI, and business stakeholders. Mentor data engineers and architects and establish reusable standards for scalable, reliable, cost-effective data products.

Owns the end-to-end lifecycle of market data, regulatory news, and symbology products. Responsibilities include roadmap development, client feedback, innovation, product delivery, revenue growth, regulatory compliance, benchmarking, stakeholder management, and supporting sales. The role collaborates with engineering, marketing, commercial, policy, regulation, and operations teams to develop and manage consumer-facing digital products.

The Senior Product Data Scientist will analyze subscriber engagement and retention, design and evaluate A/B tests, develop churn, propensity, LTV, and behavioral segmentation models, and embed with product teams to guide roadmap and feature decisions. The role requires strong SQL and Python skills, statistical and machine learning expertise, independent problem-solving, and clear communication with technical and non-technical stakeholders.

Leads enterprise data engineering strategy and global teams responsible for real-world data movement, lakehouse architecture, ingestion, transformation, storage, optimization, governance, security, and standardized data products. Partners with technology, analytics, and business stakeholders across clinical, safety, financial, and corporate domains. Oversees delivery prioritization, roadmaps, quality, reliability, team development, and scalable cloud data solutions using Azure Databricks, SQL, Python, and CI/CD.

Owns product vision, strategy, roadmap, backlog, and delivery for enterprise Finance Technology platforms. Leads AI, ML, data, integration, automation, process redesign, and digital transformation initiatives across finance, accounting, regulatory reporting, and treasury. Defines business cases, KPIs, data models, and solution proposals while managing stakeholder alignment, risk, controls, dependencies, adoption, and change management in a complex regulated banking environment.

Develop and support complex data science models and applications for retail clients, using statistical modeling, machine learning, Python, SQL, and related technologies. Translate complex business problems into scalable, reusable solutions that generate measurable customer and commercial outcomes. Collaborate with client leadership, define new workstreams, communicate insights to nontechnical stakeholders, and help shape innovative approaches in category management and retail analytics.

Leads the FCA’s Agentic AI and Data Science Research function, setting AI strategy, governance, research ethics, data science standards, and long-term capability roadmaps. The role directs horizon scanning, responsible AI adoption, model assurance, and advanced analytics initiatives supporting regulation, financial crime reduction, and organisational effectiveness. It provides senior technical advice, leads multidisciplinary teams, develops talent, and influences executive, board, regulatory, and technology stakeholders.