Top Data Jobs in London

Own the end-to-end data quality issue lifecycle, lead offshore analysts, liaise with UK stakeholders, partner with IT/Operations for fixes, drive preventive data quality improvements, and produce governance-ready reporting and dashboards.

Assist in implementing data governance frameworks and policies, ensuring data accuracy, creating data dictionaries, and contributing to data governance training and materials.

Provide EU-focused data privacy and digital regulatory legal counsel across business segments. Lead privacy program development, support HR, Marketing, M&A, and transactional work, manage incident response, regulatory engagement, DPIAs, cross-border data transfers, and advise on EU Data Act, GDPR, data governance, and product compliance.

Seeking experienced Solution Architects for UK Defence accounts to define and shape architectural solutions in digital, data, and technology services, focusing on government Defence departments.

Build and maintain ETL/data pipelines into the Data Lakehouse, develop Power BI dashboards, support AWS infrastructure (S3, Glue, Lambda, Step Functions, LakeFormation), ensure data quality using dbt, write complex SQL and Python for data processing, use Git for version control, document workflows, and collaborate with analysts and data scientists.

Lead development of behavioural features and detection algorithms from hypothesis to production using high-frequency GPS and inertial sensor data. Prototype, build scalable Python and SQL pipelines, evaluate ML and rule-based models, run experiments, and collaborate with engineering, product, and actuarial teams to integrate features into pricing and measure impact.

As a Data Architect, you will define data architectures for client programs, driving data and AI transformations, and crafting secure and scalable data solutions across various technologies.

The Product Data Science Manager leads a team to deliver custom data insights, ensuring analytical excellence and client impact while collaborating with various stakeholders.

The Data Platform Engineer will scale and modernize the data lakehouse, build data products, write ETL processes, and support analytics across the business.

Lead strategy and execution of analytics and AI/ML products to measure and maximize player lifetime value. Drive cross-team alignment, prioritize roadmaps, define experimentation and modelling standards, and ensure scalable, production-grade ML capabilities are adopted and deliver measurable business impact.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Support ledger migration and data lake cleanup by extracting, transforming, reconciling and validating large datasets. Develop and validate SQL queries, batch jobs and processing logic, perform regression testing, document mappings and validation rules, and collaborate with engineering, product and business teams to ensure production readiness and post-migration validation.

Client-facing Principal Data Engineer leading design and delivery of enterprise Databricks-based data platforms. Provide architecture leadership, mentor engineering teams, drive platform optimisation, define standards, support technical governance, and deliver across multiple consultancy engagements.

Design, build, and maintain scalable cloud-based data platform and ETL pipelines on Azure. Develop data models, ensure data quality, optimize performance, implement governance and security, and collaborate with stakeholders and data scientists to enable data-driven solutions.

As a Senior Data Scientist, you'll develop AI solutions with machine learning, mentor others, and ensure responsible AI practices while collaborating with clients to solve business challenges.

Design, build, and maintain high-volume distributed data processing jobs and pipelines (Redshift, Athena, Iceberg, Spark, Airflow). Collaborate with data scientists and analysts to productionize models, optimize performance, troubleshoot systems, and mentor junior engineers. Support lakehouse/data warehouse management and ensure data accuracy and efficiency.

Lead insider risk and data loss investigations from triage to closure, operate and tune DLP across environments, build detections and workflows, partner with HR/Legal/Compliance/IT, mature insider risk program, support audits, and mentor others.

Lead a small senior data engineering team owning the end-to-end data platform: streaming, warehousing, modelling, pipelines, ML lift, and operational excellence. Drive delivery, coach engineers, review technical designs, enforce data contracts and quality, manage costs (FinOps), and partner with Product and leadership to set roadmaps and risk. Build and grow an emerging ML function.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Maintain and create supply chain and intercompany master data in SAP and related systems for EMEA. Ensure data quality, compliance with SOPs, support product launches, troubleshoot system issues, drive process improvements (MDM/Winshuttle), and contribute to global master data initiatives while supporting costing and training materials.

Partner with product and business teams to define metrics, design and analyze experiments, build analytical frameworks, maintain governed dashboards, conduct deep-dive analyses (funnels, retention, cohorts), size opportunities, and connect product metrics to revenue to inform roadmap and leadership decisions.

As a Data Scientist, you will design and deliver bespoke AI solutions, work with clients on project feasibility, and ensure delivery excellence while acting as a technical advisor.

This role involves analyzing complex datasets, developing metrics, building dashboards, and collaborating with teams to enhance fraud detection strategies using ML models.

Design and validate agentic AI capabilities for Protege in PatentSight using ML, NLP, and LLMs. Build retrieval and hybrid search strategies, run experiments, evaluate models, analyze large IP datasets, and translate results into product recommendations while collaborating with engineers, product, and IP experts.

Design, experiment, and validate NLP/LLM and generative AI solutions for patent intelligence. Develop agentic workflows, retrieval strategies (hybrid semantic+lexical), evaluation metrics, and large-scale text processing methods. Partner with product and engineering to translate validated models into scalable, customer-facing features and deliver measurable business impact.