Top Data Jobs in London

Develops AI, automation, statistical, and machine learning solutions for insurance underwriting. The role partners with underwriters to identify business problems, build and test prototypes, structure unstructured documents and emails, analyze portfolio and market data, and create models supporting risk selection, referrals, appetite, and performance monitoring. Successful solutions are handed to central technology teams for delivery and adoption.

Designs, builds, and maintains scalable data pipelines and infrastructure supporting analytics, experimentation, and machine learning. Manages batch and streaming workflows, containerized Kubernetes deployments, data quality monitoring, observability, infrastructure as code, and CI/CD. Enables self-service data onboarding while partnering with analytics, ML, DevOps, legal, and security teams. Ensures reliable handling of regulated patient data and mentors colleagues on engineering practices.

Own complex data platform initiatives, including CDC ingestion, streaming infrastructure, BigQuery warehousing, data contracts, and reliability capabilities. Design and maintain production pipelines across AWS and GCP using Kafka, Flink, Kubernetes, Terraform, Airflow, and dbt. Establish data quality, monitoring, governance, and compliance controls while mentoring senior engineers and partnering with analytics, data science, ML, security, legal, and DevOps teams.

Design, build, maintain, and optimize data pipelines, warehouses, and data models. Ensure data quality, performance, security, governance, and GDPR compliance while supporting business intelligence and analytics. Collaborate with engineering teams to develop data-driven products, migrate to a new analytics platform, document systems, and troubleshoot data infrastructure.

Develop betting solutions using statistical models, high-performance algorithms, automation, and data processing software. Responsibilities include acquiring and cleaning data, analyzing trends, producing reports, building automated data workflows, and evaluating model performance through back-testing and simulations. The role requires a quantitative master’s degree, 1–2 years of professional experience, and proficiency in Python, R, C++, or similar programming languages.

Lead growth & adoption analytics by building and maintaining agent-ready curated datasets, partnering with engineers and product teams, reviewing merge requests, defining pipelines and metrics, performing advanced SQL analyses on petabyte-scale data, and communicating actionable insights to stakeholders.

Lead the operating model for AI and data governance requests across HR and Employee Experience. Manage coordinator teams, intake, triage, routing, SLAs, backlog reduction, escalations, reporting, and approval readiness. Support high-risk use cases through documentation, reviews, remediation, approvals, and closure. Develop governance playbooks, procedures, checklists, templates, and training while partnering with HR, technology, data, risk, legal, compliance, privacy, and governance stakeholders.

Lead business change and adoption for a Snowflake data platform programme. Responsibilities include change impact assessments, stakeholder engagement, communications, training, readiness planning, translating technical changes into business impacts, and tracking adoption risks. The consultant will work across data engineering teams and business stakeholders in a hybrid UK-based contract role.

Provide legal and compliance advice for digital, data, and AI products and services. Lead Responsible AI governance processes, including risk assessments, use-case reviews, policies, incident response, vendor diligence, and regulatory monitoring. Advise on intellectual property, data protection, product terms, commercial contracts, customer questionnaires, and negotiation strategy. Develop practical playbooks, templates, tooling, and reporting while partnering with technology, product, commercial, and sector teams globally.

Lead the ownership, strategy, and optimization of enterprise data engineering platforms, including Databricks and Azure. Design scalable cloud data architectures, pipelines, and infrastructure; automate deployment and testing through CI/CD; embed security and engineering best practices; and support operational and analytical workloads. Provide technical leadership, mentorship, roadmap ownership, and cross-functional collaboration with cloud engineers, data scientists, software engineers, and senior stakeholders.

Build and lead enterprise-wide data governance from the ground up across regulated utility service lines. Responsibilities include defining governance strategy and stewardship models, establishing data quality frameworks, selecting and implementing governance tooling, managing metadata, glossaries and lineage, developing privacy and retention controls, extending governance to AI and ML, and driving adoption through stakeholder engagement, training and change management.

Supports pricing and commercial teams by collecting, validating, analyzing, and visualizing pricing, sales, margin, customer, and product data. The apprentice will build dashboards and reports using Excel, Power BI, and SAP; assist with pricing and profitability reviews; ensure data governance; identify process improvements; and provide insights to improve revenue and margins. The role includes rotations through Pricing and Data, structured Level 4 training, and collaboration with Sales, Finance, CRM, and Commercial teams.

Lead three teams responsible for UW’s enterprise data platform, including infrastructure, data engineering, and DataSecOps. Own the GCP lakehouse migration, platform strategy, reliability, security, compliance, FinOps, AI enablement, semantic data layers, and governed data sharing. Provide hands-on technical leadership through architecture, coding, incident response, and engineering standards while managing managers, hiring, budgets, roadmaps, vendors, and executive stakeholder relationships.

Lead cost management and quantity surveying for large-scale data centre projects across the UK and Europe. Responsibilities include cost planning, procurement strategy, tender management, contract administration, post-contract management, change and risk control, forecasting, financial governance, dispute support, stakeholder management, client development, and team leadership. The role requires delivery of CSA and/or MEP packages, strong commercial expertise, and the ability to manage projects through all RIBA stages while supporting business growth and compliance.

Develop and deploy Python and SQL-based data science models for credit risk, pricing, loss forecasting, collections, and automated loan decisioning. Responsibilities include exploratory analysis, model validation, production deployment, testing, documentation, monitoring, governance, and stakeholder communication. The role partners with credit, pricing, operations, product, engineering, and data teams to deliver interpretable, technically rigorous solutions.

Manage the technical delivery and integration of multidisciplinary data centre design projects. Lead internal and external design teams, coordinate with clients and project managers, facilitate meetings and workshops, manage risks, and align design activities with procurement, planning, environmental, construction, and operations teams. Ensure projects meet scope, budget, resource, schedule, quality, and stakeholder requirements while promoting collaboration and effective design decisions.

Build and operate logistics data pipelines and integrations for carrier, shipment, invoice, and ERP data. Develop ETL/ELT workflows, document extraction, validation, reconciliation, dimensional models, and data-quality checks. Maintain pipeline SLAs, troubleshoot failures, support observability and testing, and collaborate with data scientists and software engineers. The role also involves documenting systems, onboarding data sources, and gradually taking ownership of larger platform problems.

Leads AECOM’s EMEA data centre mechanical engineering team, overseeing technical delivery, project standards, quality, resources, risk, financials, and client relationships. The role includes business development, work winning, programme management, thought leadership, and team development across hyperscale and colocation data centre projects.

Data and AI interns support analytics, AI, and data projects; analyze information to identify trends and insights; research emerging technologies; develop presentations and recommendations; and collaborate with multidisciplinary teams. The role provides practical exposure to data analysis, generative AI, machine learning, and business problem-solving while building technical, communication, and presentation skills.

Manages a specialist data team supporting defined benefit pension scheme implementations, transitions, funding, data validation, analysis, and operational handovers. Responsibilities include allocating work, ensuring quality and deadlines, developing competency frameworks, training and signing off team members, producing management information, resolving data issues, and collaborating with onboarding teams, third-party administrators, trustees, and operational stakeholders.

Design and build Mulberry’s cloud-based data platform, including ETL and ELT pipelines, data models, integrations, quality controls, governance, lineage, and semantic layers. The role uses GCP and BigQuery to support reporting, analytics, customer insight, and AI use cases. Responsibilities include architecture, troubleshooting, technical documentation, stakeholder collaboration, third-party design reviews, and technical leadership across data platform delivery.

Leads Mulberry’s enterprise data capability by defining its data strategy, operating model, governance framework, ownership model, and technology roadmap. Builds a cross-functional data community, establishes trusted definitions and quality controls, improves data literacy, and enables governed self-service analytics, automation, and AI. Partners with business, Technology, and Legal stakeholders to align data priorities with commercial value, risk management, and long-term platform strategy.

Develop data solutions and perform diagnostics and analysis for client projects. Collaborate with clients and technical teams to capture requirements, design solutions, and deliver data analytics, governance, metadata, lineage, and performance analytics capabilities using BI tools, cloud platforms, databases, ETL tools, and business glossary technologies.

Own the roadmap and backlog for internal operational tools and business administration capabilities supporting Applied Research Intelligence. Build dashboards and analyses for content delivery, partner ingestion, inventory, and operational readiness. Translate business needs into requirements, workflows, data models, and measurable outcomes while partnering with Product, Engineering, Data, Sales, and Operations. Lead process improvements, documentation, governance, release planning, and self-service capabilities that reduce manual work and improve scalability.

Lead enterprise data architecture across regulatory, finance, risk, treasury, and trading domains. Define target-state architectures, modernization roadmaps, cloud migration strategies, data models, integration standards, governance, lineage, quality, and reporting platforms. Design resilient cloud-native data ecosystems using Azure, AWS, GCP, Databricks, Airflow, and modern engineering practices. Partner with engineering, DevOps, business, risk, finance, and compliance teams to modernize legacy platforms and deliver scalable, compliant self-service analytics.