Top Data Jobs in London

Lead Pernod Ricard’s Single SKU transition and establish product and material master data governance across MDH, SAP, and local applications. Responsibilities include data cleansing, deduplication, enrichment, migration readiness, validation, cutover, workflow design, quality controls, taxonomy, stewardship, operating model development, change management, and stakeholder coordination across supply chain, factories, and technology teams. The role will evolve into leadership of a global Product Master Data team.

As a Senior Data Architect, you'll design and implement data solutions on AWS, guiding startups in creating scalable architectures and leading technical engagements.

Lead and develop a team of product designers while remaining hands-on with complex, strategic design challenges. Own design work from problem framing through production-ready delivery, establish quality standards, influence senior stakeholders across Product and Engineering, and contribute to design systems, ways of working, hiring, onboarding, and talent development.

Build and productionize machine learning models, optimization algorithms, and operational simulations for warehouse and last-mile planning. Evaluate models using rigorous statistical and business metrics, collaborate with software engineers to deploy and monitor solutions, and guide technical direction. Mentor data scientists and work with product managers, internal teams, and retail partners to deliver scalable, production-ready planning systems.

Develop and evaluate machine learning models for recommendations, personalization, customer modeling, and predictive analytics. Explore data, engineer features, conduct experiments and A/B tests, and communicate results and trade-offs. Collaborate with Product, Engineering, MLOps, Commercial, and Marketing to productionize models, monitor performance, and improve data quality. Write tested, maintainable Python and SQL using reproducible software-development practices, while responsibly applying AI-assisted tools.

Contribute to the full data science lifecycle, including data preparation, exploratory analysis, model development, validation, deployment, monitoring, and ad hoc analytics. Build data science products in Python, collaborate with underwriting, actuarial, IT, and data engineering teams, and explore generative AI use cases. The role requires statistical and machine learning expertise, SQL, data wrangling, version control, clear communication, and interest in applying models to business problems in a regulated insurance environment.

Build and maintain Microsoft Fabric data pipelines, notebooks, warehouses, lakehouses, and certified Power BI datasets. Migrate legacy Azure Data Factory, Azure Data Lake, and SQL Server solutions into Fabric while applying data governance, security, testing, monitoring, and CI/CD practices. Prepare structured and unstructured data for analytics and AI use cases, including RAG and MCP. Document solutions, meet migration milestones, and collaborate with technical and non-technical colleagues.

Designs, builds, and operates secure data pipelines and platform components using Microsoft Fabric or Databricks. Leads migration workstreams, owns ingestion from core business systems, designs warehouse and lakehouse structures, and delivers governed datasets for reporting and AI. Establishes security, version control, CI/CD, testing, monitoring, and migration practices while coaching junior engineers and collaborating with technical and business stakeholders.

Build and operate reliable data pipelines for a large-scale migration. Responsibilities include ingesting and validating varied source data, transforming extracts with SQL, Python, PySpark, and dbt, implementing automated quality checks, monitoring and recovering processing failures, and maintaining auditable data lineage and secure handling controls across cloud environments.

Analyze unfamiliar datasets for a complex data migration, identifying and quantifying data quality issues and tracing root causes. Develop reusable SQL and Python scripts for profiling, exception detection, reconciliation, and migration validation using tools such as Azure Databricks and dbt. Communicate evidence-based findings to technical and non-technical stakeholders through reports, Excel, Power BI, or Tableau, supporting mapping, cleansing, and acceptance decisions.

Build and operate scalable cloud data pipelines for quantum chemistry datasets used to train AI models. Responsibilities include transforming quantum chemistry software into cloud workflows, managing and validating scientific data, deploying containerized infrastructure through infrastructure as code and CI/CD, and ensuring reliability, security, observability, incident response, and capacity planning. The role collaborates with scientific and technical experts across disciplines.

Designs end-to-end data architectures and blueprints for complex client programs, covering ingestion, orchestration, governance, security, cost optimization, observability, analytics, and AI capabilities. Leads engineering teams, translates business needs into scalable solutions, manages strategic client engagements, supports bids and business growth, mentors consultants, and establishes technical direction. The role requires broad expertise across cloud, data platforms, architecture methodologies, and client-facing consulting.

Designs end-to-end data architectures and blueprints for complex client programs, covering ingestion, orchestration, governance, security, cost optimization, and observability. Leads data engineering teams, translates business needs into scalable solutions, advises clients on AI and modern cloud platforms, and oversees delivery. The role also involves strategic leadership, bid development, thought leadership, mentoring, and setting technical direction across consulting engagements.

The Data Steward will manage and improve enterprise master data quality, consistency, and governance for CAA’s London and international teams. Responsibilities include cleansing and enriching data, resolving quality issues, defining data requirements and authoritative sources, documenting governance standards, supporting stakeholders, and translating business needs into actionable data solutions. The role collaborates with Data Management, Product, Engineering, Data Science, and Insights teams across global time zones.

Build and operate an Azure and Databricks data platform, integrating enterprise systems such as SAP and Salesforce. Develop Terraform-based infrastructure and CI/CD pipelines, administer workspaces, environments, Unity Catalog, identity, networking, and storage, and provide secure AI capabilities including API layers, model serving, and LLM access. Own platform security, monitoring, cost control, compliance, documentation, and engineering standards.

Develops scalable data products and ingestion pipelines across Databricks medallion architecture layers. Designs data models and serving datasets for reporting, analytics, and AI use cases; implements data quality, lineage, and governance; integrates structured data and documents for AI agents; and collaborates with business and AI engineering teams. Requires production SQL and Python/PySpark experience, Databricks expertise, data modeling knowledge, and engineering discipline in Git, CI/CD, testing, and documentation.

Senior technical lead responsible for designing, owning and delivering a secure multi-region cloud data platform (OCI/Databricks). Leads platform architecture, sets engineering standards, manages delivery and mentors data engineers while supporting vendor partnerships and regulated data compliance.

Lead the development of Relay’s large-scale recommender systems for courier-route matching. This role owns recommender evolution from research to production, integrates machine learning models with operational research and network optimisation, designs experiments, and shapes the company’s research culture and technical direction.

Lead causal inference and experimentation for Relay’s two-sided logistics marketplace. Measure the effects of operational decisions on couriers over time, adapt advanced causal inference, statistical, and machine learning methods, and bring models into production. Partner with Operational Research to integrate insights into network-wide optimization while shaping experimentation standards and contributing to Relay’s research culture.

Lead operations for a new data center site from commissioning through steady-state operations. Manage technical teams, budgets, vendors, infrastructure projects, maintenance, capacity planning, incidents, risk, compliance, and operational performance. The role requires strong leadership, technical credibility, stakeholder management, and experience operating complex, high-availability infrastructure environments.

Lead analytics for global clients: develop dashboards, forecasting models and analytics tools; perform audience analysis, segmentation, performance measurement and automation; support proposals and new business; mentor junior analysts; shape analytics strategy and communicate insights to senior stakeholders.

Build and improve high-throughput data infrastructure processing billions of market data points daily. Solve distributed-systems challenges, collaborate with quantitative researchers and traders, improve platform reliability, perform incident response and root-cause analysis, and contribute through code reviews and mentoring. The role uses Python and Java alongside Kafka, ArcticDB, Airflow, Spark, and database technologies at multi-petabyte scale.

Conduct data science experimentation and evaluation for Generative AI systems, including problem framing, metric definition, baseline comparisons, error analysis, and failure-mode assessment. Collaborate with product, engineering, and business teams to develop GenAI prototypes and production-ready components. Communicate trade-offs and recommendations, apply machine learning and Python, and contribute to knowledge sharing while developing AI engineering skills.

Own the Consumer Bank KPI framework, develop trusted leadership reporting, analyze acquisition and customer value, identify trends and opportunities, challenge unsupported assumptions, and support experiment design and analysis. Partner closely with Product, Growth, Finance, and Risk teams to translate ambiguous business questions into actionable recommendations and assess sustainable growth.

Oversee day-to-day data centre operations, including critical power, cooling, security, fire suppression, maintenance, compliance, risk management, and business continuity. Lead technicians and engineers, manage staffing and performance, maintain client relationships, oversee budgets and vendors, and implement preventive maintenance and optimization strategies to maximize uptime, reliability, and operational efficiency.