Top Data Jobs in London

The Subsurface Data Manager ensures data integrity, compliance, and accessibility for subsurface data, supports exploration teams, and oversees data management processes and quality control.

Lead Data & AI strategy engagements for public-sector and regulated clients: assess maturity, prioritise use cases, build business and investment cases, design transformation roadmaps, advise on technology strategy, and support sales, delivery and capability building. Act as trusted advisor to senior stakeholders and mentor consultants to move clients from experimentation to scaled delivery.

Design, build, and maintain end-to-end data ingestion pipelines and data architecture. Transition datasets to a new stack, map/standardize/normalize data, monitor data quality, manage vendor data inputs, and partner with commercial trading teams to gather requirements and support analytics and investing use cases.

Lead the creation of Market Data Operations, manage Bloomberg and LSEG entitlements, maintain security master and identifier mappings, track enterprise data usage and costs, support exchange declarations/audits, resolve data quality issues, automate workflows, and provide stakeholder support for portfolio managers, traders, researchers and data teams.

The Project Manager will lead data center projects involving the supply, installation, and commissioning of chiller units, CRAH systems, and CDUs. Responsibilities include project planning, stakeholder and vendor coordination, scheduling, budget monitoring, risk mitigation, client communication, quality oversight, and project closure. The role requires regular travel to client meetings and data center construction sites, with occasional manufacturing facility visits.

Build and deploy commercial data science models for churn prediction, cross-sell and upsell, revenue attribution, and next-best-action recommendations. Design experiments and causal analyses, productionize models on AWS, manage feature engineering and monitoring, and communicate insights to Sales, Marketing, Customer Success, Finance, and executive stakeholders. The role requires strong Python, SQL, statistics, cloud data warehouse, and production machine learning experience.

Lead omnichannel client data workstreams within Chanel’s global PMO. Coordinate Business, IT, Legal, regional and divisional stakeholders; translate regulatory and client needs into journeys, use cases and delivery plans; manage client preference centre evolutions; monitor consent-data quality and KPIs; coordinate digital touchpoint, login and engagement initiatives; facilitate governance, decisions, communications and client-data incident remediation.

Build and maintain market, economic, research, news, and internal data pipelines for AI and quantitative models. Design scalable data architectures, implement data quality controls, manage metadata and lineage, create APIs and model-ready datasets, and optimize pipeline performance. Collaborate with data scientists and analysts while ensuring data security, privacy, permissions, and reliable integration across tools and models.

Build and optimize Snowflake data pipelines, models, and integrations using Python. Process telemetry, logs, and event data while supporting governance, access control, retention, evidence-grade data products, and reporting. The role operates within an engineering consultancy focused on cloud platforms, quality engineering, developer experience, AI, security, and scalable systems.

Leads enterprise-wide data operations and governance, overseeing data acquisition, onboarding, quality, lifecycle, releases, security, privacy, compliance, and managed data services. Develops operating models, standards, controls, reporting, and scalable processes while coordinating Data Engineering, Legal, Security, Science, Product, suppliers, and public-sector partners. Manages and grows data management and governance teams, resolves complex operational issues, and ensures trusted, efficient data delivery for health research.

Lead and grow a team of Data Scientists, Data Engineers, and ML Ops Engineers developing large-scale autonomy data pipelines and machine learning solutions for perception and planning. Define technical direction, data strategy, evaluation frameworks, and production processes. Guide data collection, curation, labeling, sampling, model deployment, and optimization while ensuring scalability, cost efficiency, safety, and quality. Collaborate globally across infrastructure, simulation, tooling, and autonomy teams.

Serve as a trusted advisor for complex consulting engagements in Data Management. Lead solution scoping, architecture, feasibility assessment, design assurance, and implementation alignment with SimCorp standards. Own global consulting practice artifacts, improve methodologies, document best practices, enable consultants, and mentor aspiring architects. Collaborate with Sales, Product Management, Value Streams, project teams, and senior clients to resolve domain challenges and influence strategic architecture decisions.

Six-month HRIS data and reporting specialist contract focused on improving Workday HCM data integrity, auditing and remediating records, rationalizing reports, building dashboards, documenting data definitions, and integrating workforce data into Looker. The role requires advanced SQL, dbt data modeling, modern data warehouse experience, and the ability to translate complex findings into clear insights for business stakeholders.

Manage and improve client data quality for a 12-month Client Discovery project. Responsibilities include validating companies, cleansing and enriching datasets, maintaining corporate hierarchies, aligning data across systems, monitoring data integrity, updating senior stakeholders, and supporting analysts with reporting and bespoke analysis.

Build and operate GTM data systems covering account identity resolution, deduplication, corporate hierarchies, account and contact sourcing, enrichment, and Salesforce synchronization. Develop LLM-powered web research and enrichment pipelines, model data in dbt on Snowflake, orchestrate workflows with Airflow, and maintain quality controls. Partner closely with Sales, Marketing, RevOps, Partnerships, and Finance to create reliable data foundations that improve targeting, pipeline management, and revenue growth.

Leads data strategy across UK client portfolios, translating business needs into data-driven media solutions and driving adoption of WPP Media platforms. Oversees platform innovation, data governance, talent development, client satisfaction, commercial performance, revenue growth, proposals, and thought leadership. Partners with senior client and agency stakeholders to communicate technical concepts, guide organizational change, and deliver measurable advertising outcomes.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.

Perform product and behavioural analysis (funnel, cohort, segmentation), define KPIs, run A/B tests, build dashboards, partner with Product and Analytics Engineering, and apply AI/ML techniques to drive product decisions and experimentation.

Lead design and evolution of the product operating model, implement AI-enabled workflows and tooling, define operational standards across product lifecycle, improve processes and knowledge management, drive adoption of modern product practices, and partner cross-functionally to enable faster, insight-driven product delivery.

Design, implement, test, secure, and maintain backend services for commercial journeys. Collaborate with product and cross-functional engineering teams, embed testing and security standards, contribute to CI/CD pipelines, and participate in engineering communities and agile delivery practices.

Lead high‑impact AI and decisioning projects from discovery through production. Define technical roadmaps, design and validate models (including causal/experimentation), partner with engineering to productionise services with monitoring and governance, mentor data scientists, and promote responsible AI practices and platform adoption.

Design, build and maintain scalable automated test frameworks and reusable test solutions. Create API, integration, end-to-end and non-functional tests integrated into CI/CD. Collaborate with engineers, product and architects to embed shift-left testing and quality by design, contribute to tooling, perform root-cause analysis, participate in code reviews and continuously evaluate tools to improve quality engineering practices.

Own end-to-end delivery of production ML and LLM-powered systems: design and build training/deployment pipelines, package and deploy models, implement monitoring for performance and drift, support batch and real-time workloads, evolve internal ML platform and tooling, promote best practices, and provide technical leadership and mentoring across data science and engineering teams.

Build, deploy, and maintain production ML and AI systems: model packaging, lifecycle automation, monitoring, batch and real-time workloads, and platform improvements while collaborating with data scientists, engineers, and product teams.

Design, operate, and optimise highly available database and storage systems to improve reliability, performance, scalability and observability. Lead platform improvements, automate operations, support data pipelines, participate in incident response and root cause analysis, and collaborate with engineering and data teams to evolve platform capabilities.