Top Data Jobs in London

The role involves shaping data and AI strategies for financial institutions, guiding clients in harnessing AI for competitive advantage, and leading workshops to align stakeholders.

Lead product strategy and delivery for Aladdin's wealth management technology. Define roadmap, embed AI and API-led integrations, drive cross-functional squads, engage clients and partners, and deliver scalable solutions for discretionary portfolios, advisory services, execution-only accounts, model management, trading, and reporting.

As an Enterprise Architect, you will lead tech advisory growth, collaborate on solutions, manage client relationships, and mentor junior consultants within the organization, driving technology transformation for clients.

The Lead Value Engineer is a trusted advisor to customers, driving their value journey through analytics, process improvement, and stakeholder engagement, particularly in Finance and Supply Chain.

The Partner Program Manager will design and manage a tiered partner program, focusing on value-added systems for partner rewards, enablement, and performance analytics.

Lead design and delivery of real-time liquidity observability and core data systems for Treasury Global Liquidity. Build scalable event-driven data platforms, collaborate with product, analysts and data scientists, implement monitoring, controls, and automation for cash management and risk, and mentor engineers to ensure high-quality, reliable production systems.

Design and deliver a real-time liquidity observability stack and core treasury functions. Build scalable, event-driven systems, implement monitoring and control mechanisms, automate cash investment and risk tooling, and collaborate with product, data scientists and stakeholders to forecast cash flows and optimize liquidity.

Lead the strategy and growth of the Data Practice at Capco, managing multidisciplinary teams and delivering high-impact analytics projects in Financial Services.

Lead data scientist for Marketing: build and maintain predictive models (LTV, MMM), model customer behavior and product usage, prioritize marketing investments, reduce measurement uncertainty, and partner with analysts and marketers to translate model outputs into actions that drive growth.

Own and operate production quantitative infrastructure for real-time FX pricing, risk, and trading. Build and maintain Python microservices, monitoring, CI/CD, shared quant libraries, and incident response. Collaborate with quants, traders, product and engineering to improve pricing, risk analytics, and trading systems while contributing to quantitative models.

Lead HR data privacy activities including managing end-to-end DSARs and privacy incidents, supporting DPIAs and RoPA maintenance, governing digital cookies/tags, building dashboards and reports, maintaining program documentation, and driving cross-functional privacy initiatives and controls.

The Senior Data Platform Engineer will manage Databricks infrastructure, develop cloud systems, collaborate with teams, and enhance machine learning workflows.

Support investment teams by acquiring, cleaning, and analyzing multi-source data to test investment theses, identify inflection points, and present research. Learn fundamental investor approaches, build decision-support tools, and communicate findings clearly to Sector Data Analysts and investment teams.

The Customer Success Manager will cultivate strong relationships with key stakeholders, develop tailored success plans, guide clients to maximize platform value, and collaborate with internal teams to achieve measurable outcomes.

Lead discovery, roadmap and delivery of data- and AI-driven products for payments: identify opportunities, build business cases, prioritise roadmaps, coordinate cross-functional Agile delivery, and support go-to-market and commercialization.

As a Senior Azure Data Engineer, you'll design and deploy data engineering pipelines using Databricks on Azure, collaborate with clients, and implement best practices in a consulting environment.

Lead architecture and delivery of data solutions on Databricks within Azure, ensuring best practices in data pipeline design and implementation.

The role involves designing and building data pipelines using AWS technologies, leading projects, and mentoring junior engineers in a data engineering context for financial services.

Design and build cloud-based data pipelines using AWS tools, collaborate with clients, ensure data governance, and mentor junior engineers.

Lead data architecture and resilience for high-volume streaming and batch pipelines. Productionize Airflow/dbt workflows for a unified customer view (CDP), own MarTech infrastructure, bridge engineering and analytics, scope ambiguous technical problems, and coach engineers on data best practices.

Build and optimize Lakehouse data pipelines (Trino + Iceberg) using Medallion Architecture to transform high-throughput event logs into audit-ready subledgers and reporting metrics. Implement stream processing with Kafka, embed automated reconciliation/data quality checks, collaborate with finance and product stakeholders, and mentor junior engineers.

Support end-to-end data lifecycle for compliance by turning business problems into validated data assets: gather requirements, create data dictionaries and mappings, map processes, write and optimize SQL, perform EDA, run UAT tests, build and maintain BI visualizations (Tableau/Mode/PowerBI), and monitor dashboard performance.

Lead data architecture and cloud transformation engagements for financial services clients. Define enterprise data strategy, design scalable cloud-native data platforms, integrate governance, cataloguing, privacy and metadata capabilities, and present technical solutions to stakeholders while shaping Capco's data architecture offerings.

The AI Portfolio Lead will develop AI product offerings, analyze market trends, manage curriculum, and ensure high-quality learning experiences while influencing policies and stakeholder engagement.

Lead implementation of data-lineage and BCBS 239 compliance across risk, finance and technology. Own data governance, quality, metadata and reporting KPIs; manage cross-functional delivery, regulatory readiness, and stakeholder communications.