Top Data Jobs in London

Design and deliver production-ready AI, NLP, and generative-AI solutions (including LLMs and RAG) for knowledge discovery. Build retrieval, ranking, recommendation, and decision-support systems, integrate scientific metadata and ontologies, establish evaluation and monitoring, write production Python code, and provide technical leadership and mentoring.

Consulting role focused on Workday Financials data migration, transformation, validation, and optimisation. Lead tenant-to-tenant migrations, design conversion templates, develop ETL and reconciliation tools, validate loaded financial data, and collaborate with clients and project teams throughout implementation.

The Solutions Consultant will implement data observability solutions, manage B2B software projects, and collaborate with customers for successful data architecture and deployment.

The Senior Software Engineer will lead the Core Data team to improve APIs, systems, and data pipelines, focusing on backend engineering and team mentoring.

Lead design, build, and operation of Rightmove's GCP-based data platform. Own ingestion, processing, storage, and tooling; develop reusable batch and real-time pipelines, infrastructure as code with Terraform, CI/CD, and promote engineering best practices across teams.

Lead design and evolution of a scalable data platform, build shared ingestion/transformation/orchestration capabilities for batch and real-time, integrate tools (dbt, Spark, Beam), implement data quality/observability/security/testing, enable AI-assisted data engineering, mentor engineers, and influence the data platform roadmap to support analytics and ML at scale.

Lead and grow a data science practice for energy and utilities clients: build and coach teams, design and scale AI/advanced analytics products, lead business development, manage high-value programmes, engage senior stakeholders, and contribute market-facing thought leadership.

Lead design and delivery of scalable analytics, BI, forecasting, and AI-driven solutions for RevOps Sales Intelligence. Partner cross-functionally to improve data quality, governance, automation, and self-service analytics using cloud data platforms and modern ML/Generative AI techniques. Manage roadmap, stakeholder communication, and operationalize AI/analytics at enterprise scale.

The role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Lead a team in developing scalable data solutions, mentor engineers, manage expectations, and contribute to code. Focus on high-quality software practices.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

Lead end-to-end SAP S/4HANA data migration activities: define strategy and governance, manage mapping, cleansing, ETL and reconciliation, execute mock and production loads, resolve defects, coordinate with testing and business teams, and support cutover readiness.

Lead customer-facing data science and AI projects for commodity markets: identify high-impact opportunities, prototype and deploy ML/AI solutions (including LLMs and agentic approaches), measure business impact, collaborate with product, engineering and domain experts, and share learnings to shape ICIS data and AI products.

Own subscription and lifecycle analytics: analyze funnels, build dashboards, perform retention deep-dives, and develop LTV and churn models. Collaborate with Growth, Product, and Editorial to translate insights into product and marketing actions that improve retention and revenue.

Design, develop and maintain conceptual, logical and physical enterprise data models. Translate business requirements into scalable data solutions, define source-to-target mappings, support relational and NoSQL implementations, optimise performance, drive data modelling standards, perform reverse engineering, and advise stakeholders on data architecture, governance and integration throughout project lifecycles.

Provide senior-level data engineering and analytics consultancy: capture requirements, design data platform and BI/advanced analytics solutions, implement ETL and data transformation, and deliver clear reports and dashboards using Microsoft and cloud data technologies.

Design and build scalable pipelines to ingest, transform and harmonise large healthcare datasets into research-ready formats (OMOP/FHIR). Lead technical direction, implement data quality and governance, collaborate with data scientists and epidemiologists, and deliver reusable data products using cloud and big-data technologies.

Build production-grade AI agents, integrations, data pipelines, and infrastructure directly within strategic customer environments. Engineers rapidly navigate unfamiliar codebases and data stacks, own technical delivery through production deployment and handoff, communicate credibly with customer engineering teams, document and train users, and feed recurring engagement insights back to Product and Engineering. The role requires 30–50% travel during active engagements and emphasizes backend systems, agent integrations, real-world deployments, and maintainable customer outcomes.

Lead data strategy and standards across product teams, develop experimentation best practices, mentor data scientists and analytics engineers, collaborate with product/engineering/design/research, and apply advanced data techniques to improve user engagement and product decision-making.

Develop and deploy machine learning and statistical models that solve business problems and create measurable impact. Responsibilities include exploratory data analysis, feature engineering, experimentation, model optimization, explainability, MLOps, prompt engineering, and cloud-based data collaboration. The role involves working with clients and stakeholders, communicating insights through visualization and storytelling, and contributing to scalable analytics, Generative AI, and data science solutions.

Lead enterprise-scale data science and AI initiatives, defining lifecycle best practices, architecture, governance, metadata frameworks, and MLOps practices. Design and deploy machine learning, statistical, optimization, and LLM solutions using cloud platforms. Lead multidisciplinary teams, translate business needs into analytical solutions, collaborate with engineers and stakeholders, and communicate insights to technical and executive audiences.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

As a Senior Data Analyst, you will analyze data, provide actionable insights, build reports, and support decision-making across departments.

Lead data migration, validation, and optimisation for Workday Financials implementations. Design and use Workday load tools, map and transform financial data, develop ETL and reconciliation processes, validate loads, support tenant migrations, engage stakeholders, and contribute to training and knowledge sharing.