Top Data Jobs in London

Analyze structured and unstructured data to answer business questions, develop evidence-based insights, create decision-oriented visualizations, and communicate clear findings to senior stakeholders. The role involves SQL-based analysis, collaboration with data engineering and research teams, AI-assisted workflows, validation of analytical outputs, and translating complex information into executive-ready narratives and recommendations.

Conduct fast, evidence-based analyses for customers, research teams, and senior stakeholders. Translate structured and unstructured data into clear findings, visualizations, recommendations, and executive narratives. Scope data needs, collaborate with engineering and cross-functional teams, use and validate AI-assisted analysis tools, identify new insights, and review junior analysts’ work.

Own the strategy, roadmap, and development of foundational data products, including governed data models and semantic layers. Partner with analytics engineering, data science, engineering, product, finance, and business teams to modernize data platforms, drive adoption, and demonstrate business impact. The role requires strong product management, stakeholder coordination, metrics definition, agile execution, and working knowledge of SQL, data governance tools, and modern data platforms.

Supports investment data, portfolio administration, and equity trade readiness across index rebalances, corporate actions, and market events. Performs data validation, reconciliation, reporting, exception resolution, root-cause analysis, and operational risk control. Partners with portfolio managers, traders, custodians, and internal teams; supports portfolio onboarding, testing, implementation, workflow improvements, and dashboards. Requires investment operations experience, derivatives knowledge, stakeholder management, communication, and familiarity with index futures, total return swaps, and currency forwards.

Supports the development, testing, deployment, and operation of machine learning solutions for DraftKings’ Sportsbook platform. Works with experienced data science and machine learning engineers on production systems, scalable model deployment, internal R&D, and technical projects. Collaborates with a mentor while learning end-to-end engineering and data science workflows.

Lead the architecture, development, and optimization of scalable batch and real-time data platforms on GCP. Design robust pipelines, data models, integrations, and secure infrastructure while improving reliability, performance, and cost efficiency. Resolve data-quality incidents, establish monitoring standards, contribute to AI initiatives, translate business requirements into technical solutions, and mentor engineers. The role requires strong SQL and Python skills, cloud data engineering expertise, Infrastructure as Code experience, and the ability to lead cross-functional technical initiatives.

Leads the strategy, roadmap, commercialization, and growth of private markets data products and feeds. Owns product P&L, pricing, partnerships, client engagement, go-to-market execution, and adoption metrics. Translates institutional investor needs into scalable data models, APIs, cloud delivery capabilities, and integrations while collaborating with engineering, data operations, sales, content, and strategic partners. The role also drives product launches, competitive positioning, strategic negotiations, and third-party data partnerships across private equity, venture capital, private credit, infrastructure, and real assets.

Lead Data Scientist supporting workforce strategy through exploratory analysis, statistical modeling, scenario planning, and visualizations. The role analyzes workforce, financial, planning, and delivery data to assess skills, capacity, sourcing, location, costs, and AI-related productivity changes. Responsibilities include validating complex enterprise datasets, documenting assumptions and limitations, developing reusable analytical solutions, and communicating evidence-based recommendations to senior stakeholders across Engineering, Finance, HR, and Procurement.

Build agentic AI solutions and scalable data pipelines from prototype through production. Partner with business stakeholders, develop and deploy AI agents as secure containerized services, implement CI/CD and infrastructure automation, integrate cloud resources and databases, maintain data quality, and monitor agent performance. The role also involves creating interactive demos, supporting workflow integration, and measuring business impact.

Leads enterprise Data and AI consulting engagements, including strategy, operating models, cloud data platform migrations, AI model governance, and large-scale transformation programs. Partners with C-suite stakeholders, oversees delivery from roadmap through implementation, facilitates executive workshops, and mentors multidisciplinary consulting teams. Requires expertise with Snowflake or Databricks, SAS modernization, cloud architectures, AI governance, model deployment, and executive advisory. Preferred experience includes cloud certifications, P&L or pre-sales leadership, regulated industries, Generative AI, and MLOps.

Supports development and maintenance of a modern data platform and lakehouse. Builds ingestion and transformation workflows, implements data quality checks, schema enforcement, logging, monitoring, and lineage, and assists with data modeling, governance, security, troubleshooting, and platform maintenance. Collaborates with data, analytics, and business teams in an agile environment while developing technical skills under senior engineer guidance.

Build and maintain scalable batch and near-real-time data pipelines, reusable data models, and production-grade data infrastructure. Improve platform architecture, CI/CD, infrastructure as code, data quality, testing, governance, observability, and cost efficiency. Collaborate with Data Science, MLOps, Product, and commercial teams to deliver reliable AI and audience intelligence solutions while mentoring colleagues and communicating with stakeholders.

Build and operate an Azure-based data and AI platform through hands-on infrastructure engineering. Responsibilities include designing Azure landing zones and networks, managing governance and identity, deploying Databricks and data services, building MLOps pipelines, implementing DevSecOps practices, creating self-service tooling, and supporting platform incidents. The role requires Terraform, Azure, Databricks, CI/CD, containerization, security, observability, and Python or PowerShell expertise.

Build and operate a production Azure data and AI platform. Responsibilities include Azure landing zones, network architecture, identity, governance, monitoring, Databricks and Unity Catalog administration, data integrations, MLOps pipelines, model deployment, CI/CD, infrastructure as code, security scanning, self-service tooling, documentation, and on-call support. The role requires hands-on Terraform, Azure, Databricks, containerization, observability, and Python or PowerShell experience.

Leads RSM UK’s people data, management information, reporting, governance, and analytics capability. Owns data quality standards and controls, develops dashboards and workforce insights, and builds predictive analytics for workforce planning, retention, and business performance. Partners with senior People, Finance, IT, and business stakeholders to translate complex analysis into strategic recommendations, improve reporting automation, promote responsible AI and ethical data use, and increase data literacy.

Design and govern enterprise data architecture across ERP, CRM, finance, supply chain, and laboratory systems. Create conceptual, canonical, dimensional, semantic, and ontology-based models; define integration and ELT patterns; guide Snowflake and dbt implementations; establish data governance, quality, metadata, lineage, and master data practices; and enable trusted analytics, AI, RAG, and GraphRAG applications.

Designs, builds, and operates scalable data pipelines, models, and solution architectures on GCP. Owns technical delivery from scoping through production, including testing, observability, data quality, incident resolution, and cost optimization. Collaborates with clients, consultants, and data scientists to translate business needs into reliable systems. Leads project decisions, communicates trade-offs, reviews code, documents solutions, and uses AI-assisted engineering tools responsibly.

The Data Scientist designs and deploys data models and automation solutions, collaborating with teams to meet business needs through data-driven insights.

Own the vision, roadmap, regulatory delivery, data services, and backlog for Citi’s XVA Quattro and Canvas technology platforms. Translate capital markets, risk, quantitative, and regulatory requirements into technical roadmaps, epics, and user stories. Partner with trading, risk, quant, architecture, and engineering teams on prioritization, Agile delivery, UAT, releases, resilience, and governance. Champion Generative AI for requirements, testing, documentation, operational efficiency, anomaly detection, and platform reliability.

Build and maintain Ocado’s foundational data warehouse and scalable ELT/ETL pipelines. Develop dimensional models and reusable transformations, implement data quality checks and monitoring, optimize cloud warehouse performance, and deliver secure, trusted datasets for analytics, reporting, machine learning, and partner products. Collaborate with data scientists, analysts, product owners, and platform engineers in an agile environment while contributing to code reviews, documentation, testing, and engineering improvements.

Own the vision, roadmap, backlog, regulatory delivery, and data platform enhancements for Citi’s XVA Quattro and Canvas platforms. Translate capital markets, risk, and regulatory requirements into technical product plans, user stories, and acceptance criteria. Partner with trading, risk, quantitative, architecture, and engineering teams to deliver resilient solutions. Lead AI-enabled requirements, testing, operational improvements, UAT, release governance, and regulatory compliance across distributed Agile teams.

Designs, develops, tests, deploys, and supports scalable data pipelines, integrations, and data products on Quilter’s Azure-based platform. The role uses Databricks, Azure Data Factory, Data Lake Storage, Spark, and related technologies to support analytics, reporting, regulatory, and operational needs. Responsibilities include data governance, security, quality, CI/CD, monitoring, incident resolution, performance optimization, and stakeholder collaboration within Agile teams.

Lead technical design and delivery of scalable data platforms, pipelines, data products, and engineering standards. The role requires hands-on Databricks and Azure expertise across batch and streaming architectures, data governance, modelling, security, observability, CI/CD, and infrastructure automation. It also involves mentoring engineers, reviewing architecture and code, resolving complex technical issues, collaborating with architects and stakeholders, and improving data quality, reliability, and platform performance.

Own delivery of capital data products from sourcing and ingestion through transformation, controls, and distribution. Translate capital reporting and measurement needs into product increments, backlogs, user stories, data quality rules, schemas, metadata, lineage, and operational requirements. Partner with engineering, governance, and capital stakeholders to improve data reliability, usability, regulatory controls, and AI readiness. Support agile delivery, consumer enablement, stakeholder communication, and potential progression into a Data Product Manager role.

Designs, administers, secures, and optimizes enterprise SQL database environments across on-premises, cloud, and hybrid platforms. Responsibilities include SQL Server architecture, high availability, disaster recovery, performance tuning, Azure database management, migrations, upgrades, security, governance, capacity planning, documentation, incident resolution, and technical mentoring. The role collaborates with developers, infrastructure, cybersecurity, and business stakeholders to deliver reliable, scalable database solutions.