Top Data Jobs in London

As a Software Engineer in Data and Lifecycle, you'll design and implement solutions for trade automations, maintain reference data, and collaborate with stakeholders.

Seeking a Software Engineer for the Data and Lifecycle team to automate trade processes, maintain reference data, and collaborate with stakeholders on derivatives.

Handle complex, custom client requests by building end-to-end reports, performing quantitative analysis and data mining using SQL/pySpark and Python, developing research tools on Similarweb raw data, and partnering with Engineering and Advisory teams to scale solutions and deliver strategic data products.

The role involves owning product feature development, participating in system design, and maintaining software performance in an agile setting.

The Data Mentor provides technical support and feedback to apprentices, ensuring high standards in teaching, learning, assessment, and learner progression monitoring.

As an Energy Consultant, you'll bridge Gorilla's SaaS solutions with energy market needs, drive pre-sales engagements, and support deal strategies through workshops and tailored presentations.

Measure and optimize underwriting using statistical analysis and machine learning on claims, risk signals, and underwriting data. Build models for risk assessment, pricing, and automation; create dashboards and reports; collaborate with actuarial, product, and engineering teams; lead initiatives and mentor team members to improve underwriting efficiency and risk selection.

The Director Analyst focuses on data integration and engineering, providing insights, advice, and research to help organizations improve their data practices and strategies.

The Data Engineer will design and support data warehouses, create analytics dashboards, perform data quality analysis, and identify process improvements. Responsibilities include collaborating with stakeholders to develop insights and ensure data governance.

Lead the Finance & Regulatory Data team, overseeing finance data strategy, governance, and analytics to support corporate finance and audits.

As a Senior Data Engineer, you will design and develop large-scale data processing software, manage technical components, and mentor team members, while ensuring code quality and operational readiness.

Use SQL and Python to analyse transaction monitoring and fraud rule performance, identify patterns and anomalies, reduce false positives, and prioritise high-risk cases. Partner with FinCrime, Product, Compliance and Data teams to improve controls, data pipelines and detection logic, support scoring and future ML use cases, and translate analysis into actionable recommendations that balance fraud prevention with customer friction.

Lead ownership of private markets data model and architecture, manage integration/connector inventories, design data migration and integration frameworks, advise clients and support sales, and bridge advisory and technical delivery teams to enable BI and enterprise performance solutions.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Lead investigation and documentation of reference data quality issues across FIC asset classes, perform root-cause analysis, map data flows and ownership, maintain issue logs, support remediation roadmaps and prioritisation, collaborate with business and technical teams, and produce clear data documentation.

Design, build, and maintain scalable analytics models and automated reporting. Partner with Sales, Product, Marketing, and Finance to define KPIs, deliver ad hoc analysis, improve data quality and observability, and establish analytics engineering best practices to enable self-service analytics.

Lead QA and test strategy for an AWS-native big data platform. Define automated test frameworks, validate large-scale data pipelines (PySpark/EMR), integrate tests into GitLab CI/CD, manage risk and reporting, mentor QA teams, and ensure data integrity across cloud data lake workflows.

The Data Centre Engineer will manage data centre operations including design, installation, and maintenance of equipment while ensuring data safety and accessibility.

Lead end-to-end ownership of the data estate and delivery of large data transformation programmes. Manage stakeholders, vendors, budgets, SDLC, data platform architecture, compliance, and a cross-functional team to deliver Snowflake and relational database solutions aligned to business strategy, especially for insurance.

Design, build and optimise scalable cloud-native data platforms and pipelines using Microsoft Fabric, Azure Databricks, Synapse and ADF. Implement ETL/ELT with PySpark and SQL, ensure data governance, performance, reliability, and collaborate with data scientists and engineers to deliver production solutions.

Lead CuspAI's data strategy and team to acquire, generate, curate, and deliver ML-ready materials data. Build partnerships, prioritize data investments, oversee data-generation programs, set quality/provenance standards, and act as single point of accountability for research data commitments.

Lead cross-functional squads to define and execute data product strategy for life sciences. Drive high-throughput data pipelines, curation tools, data APIs, analytics, and GenAI solutions. Establish success metrics, QA frameworks, engage stakeholders, and align roadmaps with engineering and product teams.

Design and own data architecture and ingestion pipelines that convert wet-lab instrument outputs into versioned, reproducible datasets for ML and RL training. Build tooling for dataset inspection, lineage, and integration with model training workflows, and collaborate closely with ML researchers to support pretraining, midtraining, and RL experiments.

Contribute to marketing effectiveness and data science projects: clean and prepare data, build and apply MMM and predictive models, create visualizations and dashboards, support client meetings, and deliver actionable media optimization recommendations.

Lead product-focused analytics and experimentation to uncover growth opportunities, define KPIs, analyze user behavior and retention, quantify feature impact, and communicate data-driven recommendations to cross-functional stakeholders.