Top Data Jobs in London

Own the SFMC-to-Braze migration and develop a trusted marketing data source of truth across 40+ tools. Responsibilities include data architecture, schema and identity resolution, SQL and Python validation, pipeline debugging, data quality monitoring, AI-assisted workflow automation, vendor management, and cross-functional coordination with data and technology teams.

Own data science and analytics for the Shipping team, analyzing buyer and seller behavior, delivery performance, carrier economics, and product outcomes. Design and evaluate A/B tests, build dashboards and scalable datasets, debug data issues, and provide strategic recommendations. Partner with product, operations, engineering, platform, and machine learning teams to improve shipping strategy, experimentation, and data-driven decision-making.

Lead a virtual pharmacology modeling program by evaluating biological datasets, building data pipelines and machine learning proof-of-concept models, establishing scientific validation criteria, and preparing models for internal and commercial use. Collaborate with laboratory scientists, senior leadership, IT, and AI teams. Ensure reproducible, documented code and models, identify data gaps, guide additional experiments, communicate results to diverse audiences, and potentially build a data science team.

Leads Ofcom’s research innovation and data strategy, overseeing statistics, analytics, behavioural research, innovative research methods, and programme management. The role develops responsible data and AI capabilities, improves access to insight, strengthens cross-organisational collaboration, manages senior teams and budgets, and represents Ofcom externally. It requires strategic leadership, advanced data science and research expertise, data ethics knowledge, programme management, change leadership, and the ability to communicate complex analysis to regulatory, policy, commercial, and external stakeholders.

Build, maintain, and release reliable cloud software for the Customer Data Platform. Lead complex feature delivery, make architectural decisions, improve code quality, testing, observability, security, and operational reliability. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and OKRs, and mentor engineers through pairing, reviews, and technical guidance.

Own and enhance data intelligence products, BI solutions, dashboards, KPI frameworks, data governance, master data management, and workflow automation. Translate business objectives into actionable insights, scalable architectures, roadmaps, and measurable commercial outcomes. Analyze travel and meetings data, improve data integrity, document systems and processes, support client implementations, and collaborate with Sales, Finance, Technology, and leadership teams.

Own and improve matching, recommendation, and taxonomy-mapping models for Beamery’s talent data platform. Responsibilities include building evaluation datasets and metrics, conducting scientific experiments, tracking technical and business performance, writing tested Python code, reviewing AI-generated code, researching LLM and recommender-system advances, and communicating findings to product, engineering, and business stakeholders.

Lead fifty-five’s UK business and London office, owning growth strategy, client relationships, P&L, people leadership, partnerships, and resource allocation. Drive opportunities across AI, data, cloud, and marketing transformation while strengthening Google and Google Cloud partnerships. Represent the company through industry relationships, events, PR, and thought leadership, and collaborate with Brandtech Group leaders to expand the UK business.

Analyze consumer product behavior, design and evaluate A/B tests, define product metrics, and build reusable analytical datasets and dbt models. Use SQL, Python, Snowflake, Dagster, and ETL pipelines to support reliable analysis. Partner with Product, Engineering, Design, and Growth to influence activation, engagement, retention, and monetization decisions. Independently solve ambiguous problems and communicate recommendations to technical and nontechnical stakeholders.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Processes and maintains investment market, master, and corporate-action data in SAP FAM to support accurate investment accounting. The role documents procedures, responds to finance and investment operations inquiries, supports projects, and improves data-control processes and systems. Candidates should understand investment products, accounting treatments, control environments, investment operations systems, and market-data platforms.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Reconstructs and redesigns data-driven presentations in Microsoft PowerPoint, creating visualizations and analytics reports that communicate complex insights clearly. Reviews and edits content for accuracy, flow, and narrative impact; applies data analysis and statistical reasoning; and refines slides based on feedback. The role supports AI training projects through high-quality presentation and analytical deliverables.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Leads Gartner Consulting’s Data and AI practice by developing client relationships, managing strategic engagements, advising executive stakeholders, creating consulting assets, and driving sales and revenue growth. Builds and mentors high-performing teams, develops AI and data transformation offerings, conducts market and competitive analysis, and provides thought leadership across data strategy, analytics, governance, AI, and emerging technologies.

Build and maintain scalable data infrastructure for autonomous-driving applications. Responsibilities include developing vehicle-fleet data ingestion pipelines, distributed cloud systems, data processing and transformation services, workflow orchestration, and reliable data platforms. Collaborate with computer vision and machine learning engineers to support large-scale ML development, validation, and simulation. The role requires strong Python or object-oriented programming, distributed application development, cloud experience, RPC protocols, testing, observability, and data pipeline expertise.

Analyze Gecko browser engine telemetry across Desktop and Android, build leadership dashboards, identify telemetry gaps, develop frameworks to remove bot and hardware noise, and correlate performance metrics with user behavior. The role requires advanced SQL, statistical analysis, data visualization, browser component knowledge, and independent problem solving to translate engineering data into product strategy.

Build and maintain a near-real-time financial forecast dashboard using Flourish and Canva. Integrate Binance and Bloomberg APIs, automate financial modeling and data updates, translate complex metrics into visual narratives, and optimize dashboard accuracy and responsiveness. Collaborate with advertising, design, marketing, and analytics teams to ensure brand alignment and client-focused user experience.

Maintains and improves vessel particulars datasets by researching and validating maritime data, identifying anomalies and gaps, automating data workflows with SQL and Python, developing quality dashboards, supporting data-source integration, and resolving data issues. The role collaborates with product, engineering, market analysts, and data management teams to strengthen data accuracy, consistency, coverage, and pipeline reliability.

Own and improve maritime company and vessel ownership datasets. Research, validate, cleanse, and enrich large datasets using SQL and Python; identify anomalies and data gaps; develop quality checks, dashboards, and reports; integrate data sources; resolve issues; document business rules; and collaborate with Product, Engineering, Market Analysts, and data teams to improve platform capabilities.

Leads software engineering for an AI/ML data platform, designing, developing, testing, troubleshooting, and operating secure, scalable Python applications in containerized cloud-native environments. Partners with product and data teams, promotes responsible AI-assisted development, automates recurring remediation, evaluates technology solutions, and mentors junior engineers.

Develop and support secure, scalable software for AI/ML data platforms. Responsibilities include Python architecture and development, cloud-native and containerized application delivery, code review, testing, troubleshooting, operational stability, and automation of recurring remediation. The role also promotes validated AI-assisted engineering practices and collaborates with product managers and data strategy teams. Preferred experience includes graph databases such as AWS Neptune and generative AI engineering pipelines.

Evaluate and onboard macro financial datasets for quantitative research and trading. Assess data coverage, quality, consistency, APIs, and delivery mechanisms; develop Python tools and pipelines to extract, clean, normalize, and analyze data; investigate discrepancies across providers; improve dataset quality; and collaborate with researchers, traders, vendors, and engineering teams to automate reliable data integration.

Lead FactSet’s global privacy program and serve as senior legal counsel on technology, AI, data protection, cross-border transfers, privacy risks, and cybersecurity incidents. Advise product, engineering, compliance, and security leaders on GDPR, CCPA, AI governance, data licensing, and privacy-by-design. Manage regulatory engagement, privacy training, data subject rights, impact assessments, data mapping, and contract terms across multiple jurisdictions.

Manage the organization’s global data privacy program, ensuring compliance with GDPR, CCPA, HIPAA, UK and US privacy laws, and emerging AI regulations. Conduct audits, risk assessments, DPIAs, TIAs, LIAs, and vendor reviews; maintain processing records and privacy documentation; advise teams and clients; handle data subject requests and regulatory communications; support breach response and AI governance; and deliver privacy training and awareness programs.