Top Data Jobs in London

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.

Build and enhance AWS QuickSight reporting for Customer Service, create reusable datasets, integrate business data sources, and establish consistent metrics and documentation. The role translates stakeholder needs into dashboards, automates manual reporting, validates data quality, investigates issues, and supports self-service analytics and knowledge transfer during a six-month engagement.

Own and build the company’s metadata-driven data layer as a hands-on principal architect. Design schema-flexible and versioned platforms with lineage, auditability, semantic mapping, and resilience to schema changes. Write production code, establish AI-assisted engineering practices with verification, coach engineers, and communicate architecture to technical and business stakeholders. Experience with regulated data, multi-tenant SaaS, semantic technologies, or clinical data standards is valuable.

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Lead complex engagements in Life Sciences Consulting, managing project teams and relationships, driving strategy and financial outcomes, while fostering a culture of excellence in the team.

Leads technical strategy and pre-sales engagement for hyperscale cloud, AI, and data center networking solutions. Partners with customers, hyperscalers, technology providers, sales teams, and product organizations to co-design architectures, shape go-to-market strategy, and influence product development. Requires deep expertise in routing, switching, SDN, network automation, virtualization, and hyperscale infrastructure, plus strong executive communication, RFP response, and relationship-building skills. Travel of up to 50%, including international travel, is required.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Lead Funding Circle’s data engineering and platform architecture across Snowflake and AWS. Own the future-state data platform strategy, migration roadmap, and scalable warehouse, lakehouse, streaming, and analytics solutions. Define data product standards, governance, quality, lineage, observability, and architecture decisions. Partner with Product, Engineering, Data Science, and Governance teams while mentoring engineers and enabling AI-focused data solutions.

Build and monitor credit scorecards, develop lending strategies, run experiments, evaluate alternative data, analyze portfolio performance, and support fair, compliant model governance. Own projects end-to-end from data exploration and modeling through stakeholder recommendations and production deployment, partnering closely with engineering and credit leadership.

Drive Fluke’s business development and revenue growth across the European data centre ecosystem. Build strategic account plans, generate pipelines, win new logos, expand product adoption, and secure multi-site agreements with data centre operators, contractors, utilities, EPCs, and power-generation companies. Develop customer workflows, influence technical specifications, lead executive engagements, support channel partners, provide product feedback, and represent Fluke at industry events while serving as the company’s data centre subject matter expert.

Design and own scalable, secure data architectures and pipelines. Translate business needs into technical designs, set standards, guide engineering teams, ensure performance, reliability, security, CI/CD, and maintain architectural documentation across global teams.

Lead and grow Preply’s Data Platform engineering team, defining the Lakehouse, ingestion, governance, data quality, and reliability strategy. Partner with Analytics, ML, Data Science, Product, Security, and Engineering leaders to prioritize platform investments, guide architecture, improve operational excellence, and deliver trusted data products. Responsibilities include coaching engineers, hiring, setting SLOs, managing incidents, driving execution, and evolving team structure and technical standards.

Develop and optimize AI and computer vision models for real-time exercise tracking, form correction, repetition counting, and feedback. Analyze pose-estimation time-series data, create real-time algorithms, handle occlusion scenarios, and optimize models for hardware accelerators. Collaborate with data science, engineering, product, QA, customer support, and marketing teams to deliver reliable fitness product features.

Manage and mentor Data Scientists while overseeing the full lifecycle of machine learning models used for real-time payment decisions. Shape the Payment Intelligence roadmap, guide production-grade model development on GCP and Vertex AI, collaborate with engineering, product, and design teams, and communicate ML strategy and business value to senior leadership. The role focuses on fraud prevention, payment optimization, and scalable fintech innovation.

Leads enterprise software sales for BNY Data and Analytics across the UK and Europe. Responsibilities include generating and qualifying leads, building client relationships, identifying decision-makers, closing business, meeting sales quotas, supporting sales plans, and driving revenue and customer satisfaction. The role requires financial services and buy-side investment industry experience, particularly selling data management, accounting, and performance solutions, along with strong presentation skills and extensive regional travel.

Leads Zeta’s UK enterprise customer-growth strategy across retention, expansion, executive relationships, and delivery. Builds and manages a Key Account Director organization, establishes strategic account plans, coordinates cross-functional teams, identifies expansion opportunities, manages customer risk, and improves revenue retention and product adoption. The role develops repeatable customer-growth playbooks, strengthens executive engagement, and scales the model across EMEA.

Establish and scale data stewardship across Technology and Operations by driving data ownership, governance, metadata, lineage, quality standards, and accountability. Support data product onboarding and lifecycle management within the Data Marketplace, monitor quality metrics, resolve governance gaps and data risks, and collaborate with business, product, engineering, and domain teams to deliver trusted, discoverable, reusable, and AI-ready data products.

Build and scale a centralized data platform for investment teams. Responsibilities include gathering requirements from portfolio managers and researchers, evaluating structured and unstructured financial datasets, developing reliable processing workflows, improving data infrastructure, and creating reusable tools for analysis and quality checks. The role requires Python programming, familiarity with ETL and data pipelines, relational databases or SQL, and a STEM degree.

Lead the organisation’s AI strategy and data function, driving AI adoption, workflow redesign, self-service analytics, semantic data modelling, and trusted data access. The role manages Data Analysts, oversees the business-facing data warehouse layer, establishes analytics engineering standards, and delivers measurable insights and business value. It requires strong experience implementing AI solutions, cloud data platforms, SQL, dbt, financial services data, and stakeholder leadership in regulated environments.

Designs and delivers enterprise data solutions using Microsoft Fabric, from discovery and prototyping through architecture and implementation. Builds data products and platforms supporting reporting, analytics, and AI using pipelines, notebooks, lakehouses, and warehouses. The role involves SQL and Python development, architectural decision-making, customer engagement, mentoring colleagues, and contributing to the data and AI community. Familiarity with capacity management, metadata-driven platforms, data modeling, Git, AI-driven development, Azure DevOps, and infrastructure is expected or desirable.

Own end-to-end data science initiatives for a wholesale marketplace, including predictive modeling, ranking, demand forecasting, pricing, experimentation, segmentation, matching, and LLM-powered features. Translate ambiguous commercial problems into measurable ML and statistical solutions, deploy and monitor production models, conduct A/B tests, and collaborate with Product, Engineering, Finance, and Commercial teams.

Leads business analysis for global data, reporting, analytics, and data-platform initiatives. Gathers and translates business requirements into functional specifications, user stories, process maps, dashboards, data products, and delivery outcomes. Coordinates stakeholders, solution definition, testing, user acceptance, adoption, governance, data quality, security, and ongoing enhancements. Supports Microsoft Fabric, Power BI, ETL, data warehousing, semantic models, and governed analytics capabilities.

Leads and grows a data science team while remaining hands-on with coding, machine learning, data collection, clustering, attribution, heuristics, and model governance. Defines research direction, hires and develops scientists, builds scalable datasets, ensures model validation and monitoring, and partners across Intelligence, Product, and Engineering. Requires strong Python and SQL skills, modern cloud data-stack experience, AI fluency, technical leadership, and the ability to work with large behavioral or transactional datasets.

Deliver digital, data, and technology strategy engagements for clients across asset-intensive sectors. Develop data strategies, digital roadmaps, transformation recommendations, and strategic insights; lead workshops, gather requirements, analyze operational and financial data, and support technical solution delivery. Contribute to bids, proposals, client presentations, business development, service propositions, and knowledge assets while collaborating with multidisciplinary teams and mentoring colleagues. Travel up to 2–3 days per week is expected.

Build and operate a centralized game event data hub by developing data ingestion, preparation, orchestration, quality checks, backfills, metadata management, monitoring, and alerting. Collaborate with data engineers across studios to align shared solutions with existing patterns. Contribute to productionizing, monitoring, and maintaining machine learning pipelines as responsibilities expand.