Top Data Jobs in London

Lead data integration activities for Kinaxis solution deployments, including data mapping, transformation, loading, testing, validation, system configuration, and deployment. Design integration solutions, define requirements, support customer workshops and training, and collaborate with architects, support, and cloud teams. Advise customers on integrating Kinaxis Maestro with enterprise systems using ETL tools, APIs, databases, and ERP connections. The role is remote in the UK and may require up to 70% travel.

Build Terrific’s monetization and commerce data products from the ground up. Responsibilities include designing event and identity models, consent and attribution systems, reconciling reporting data, creating advertiser-facing campaign and reporting tools, delivering sponsored placements, and integrating external buying platforms. The role combines hands-on backend and data engineering with product ownership, customer collaboration, technical specifications, build-versus-buy decisions, and independent judgment.

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.

Lead the architecture and evolution of a Snowflake-based operational data platform. Design secure, governed, scalable data warehouse solutions, real-time ingestion, API integrations, AI-enabled capabilities, and natural-language querying interfaces. Define data flows, schemas, roadmaps, architectural standards, and governance while collaborating with engineering, data, business, and executive stakeholders. Troubleshoot complex platform issues, guide technical direction, and promote modern data engineering practices across enterprise systems.

Leads architecture and evolution of Snowflake-based operational data platforms, including real-time ingestion, AI-enabled capabilities, API integrations, data modeling, governance, and secure enterprise data flows. Partners with engineering, data, and business stakeholders to define roadmaps, standards, and technical direction while troubleshooting complex systems and communicating architecture decisions to technical and executive audiences.

The Sales Manager will sell data platforms and healthcare solutions to UK customers, developing relationships, managing customer issues, and discovering sales opportunities.

The Senior Data Modelling Consultant will design enterprise-grade data models, support data migrations, and enforce data governance standards for financial services clients.

As a Data Analytics Principal Consultant, you'll lead data transformation initiatives, mentor consultants, and engage with stakeholders in financial services to drive data literacy and innovation.

Leads technology strategy, roadmap, governance, and delivery for digital marketing, content, web, analytics, and AI capabilities across Gilead’s Ex-US Commercial organization. Partners with Commercial, Medical, Legal, Compliance, and IT stakeholders to deliver scalable solutions using Salesforce Marketing Cloud, Veeva Vault PromoMats, AWS, Databricks, and other platforms. Owns product lifecycles, vendor management, demand planning, analytics, AI enablement, and compliant digital transformation.

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 large-scale technical initiatives and build scalable product functionality and infrastructure using TypeScript, React, and Node.js. Partner with product and design teams to develop privacy features for SaaS customers, make architectural tradeoffs, mentor engineers, support hiring, and establish engineering excellence across a growing organization.

Develops and supports business intelligence solutions, including data extraction, data warehouse ETL, dimensional modeling, cubes, reporting, data analysis, and SQL performance tuning. Maintains scheduled processes and BI environments, resolves service issues, supports business reporting needs, and collaborates with underwriting, claims, finance, and management information teams. The role also contributes to data quality, reporting capability, team development, and future BI strategy.

Own electrical engineering and project delivery for energy infrastructure serving data centres and industrial customers. Responsibilities include managing development programmes and budgets, grid connection processes, electrical design, consultant and contractor coordination, design reviews, risk management, procurement, commissioning, energisation and handover. The role requires MV/HV infrastructure experience, GB or European grid connection expertise, multidisciplinary design coordination and accountability across first-of-a-kind power projects.

Designs end-to-end enterprise data solutions spanning integration, warehousing, master data management, governance, and analytics. Translates business requirements into scalable architecture, data models, integration patterns, data flows, and technical designs. Partners with engineering and application teams, establishes architecture standards, ensures security, privacy, quality, lineage, and regulatory compliance, and provides architectural leadership across complex, cross-functional initiatives.

Designs, develops, and supports secure cloud-based data integration solutions using Microsoft Fabric and Azure. Responsibilities include client discovery, requirements gathering, ETL and ELT development, REST API and SFTP integrations, data pipeline implementation, production monitoring, incident investigation, and reliability improvements. The role collaborates with architects, analysts, project managers, and delivery teams across multiple client engagements.

Own production NLP and applied AI systems that enrich scientific literature with entities, classifications, claims, and summaries. Design and evaluate classical NLP, embedding, retrieval, and LLM approaches; build scalable Python pipelines; manage cost, concurrency, reliability, and model quality. Collaborate with data engineers on Airflow and Dagster orchestration, contribute to agentic AI applications, and work with editors, product managers, and engineers to deliver research intelligence to users.

Build and operate NLP enrichment pipelines over scientific literature, extracting entities, classifications, claims, and summaries. Compare classical NLP, retrieval, embedding, LLM, and fine-tuned model approaches using evaluations, cost, quality, and operational metrics. Create golden datasets, define evaluation strategies, contribute to agentic AI applications, and collaborate with editors, product managers, engineers, SMEs, and vendors to deliver production modeling systems.

Analyze data, identify trends, create reports and dashboards, support data governance and quality, gather business requirements, improve processes, and contribute to change and transformation initiatives. The role requires strong analytical, problem-solving, communication, and stakeholder engagement skills while working on high-profile client projects and potentially working on-site up to five days per week.

Provide first-line data support to quantitative researchers and traders, troubleshoot data access and quality issues with data engineers, manage JIRA workflows, document data processes, onboard and train users, and help expand data coverage through live collection and backfills.

Owns delivery of multiple mechanical projects, works packages, and variations across live data centre environments. Responsibilities include full lifecycle project management, programme and commercial oversight, design coordination, client and stakeholder management, subcontractor coordination, risk mitigation, commissioning, handover, HSEQ compliance, and team leadership. The role is site-based in Canary Wharf and supports projects across London and the M25, with occasional travel to other client locations.

The Data Scientist will turn messy media and audience data into business insights and decisions. Responsibilities include sourcing and cleaning data, analyzing trends and audience behavior, designing experiments, building and deploying forecasting and content-analysis models on AWS, maintaining trusted dashboards and reports, and applying LLMs and agentic coding tools. The role works autonomously with engineering, product, business, and company leadership in a fast-paced remote startup environment.

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.

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.

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.