Top Data Analyst Jobs in Manchester

The Data Analyst role involves delivering RMA policies, engaging stakeholders, analyzing data within control frameworks, and recommending improvements, with a focus on revenue and risk management.

Perform deep-dive analyses, build and maintain Power BI dashboards, define KPIs, support A/B testing and AI measurement, enable self-service analytics, and collaborate with stakeholders and engineering to ensure reliable data and actionable insights.

As a Data Analyst III, you will analyze data, create visualizations, and provide insights using AI tools to support business decisions while collaborating with stakeholders.

The Data Analyst will transform raw data into actionable insights, develop data models, ensure data quality, and create visualisations for strategic decisions.

The Client Data Analyst acts as a liaison for client program management, focusing on data analysis, reporting, and supporting travel program operations.

The Finance Systems Data Analyst supports the Finance organization by ensuring data accuracy across SAP and Snowflake, optimizes data flows, automates reporting, and enhances financial decision-making.

The Finance Systems Data Analyst ensures accuracy and accessibility of financial data across SAP and Snowflake, optimizing data flows and reporting.

As a Graduate Data Analyst, you'll analyze data to drive business decisions, create visualizations, and develop automated reports in a fast-paced environment.

As a Data Analyst at Thrill Labs, you will analyze datasets to provide insights, develop dashboards, and collaborate with teams to drive product and operational strategies.

Analyze on-chain trading activity and market/DEX/CEX data at scale, build models for portfolio/PnL and capital efficiency, design AI-driven research pipelines, produce insights to inform product and capital strategy, and collaborate with product and engineering teams.

The Data Analyst role involves developing automated reporting workflows, analyzing media quality, and providing insights to clients. Responsibilities include SQL querying, Excel modeling, and collaboration with teams to ensure data accuracy and deliver insights.

The Senior Research Data Analyst conducts statistical analyses in health outcomes research, develops analysis plans, and supports study operations, including protocol development and literature reviews.

As a Platform Data Analyst, you'll monitor performance, build analytics dashboards, and ensure reliable data pipelines for the Runware platform.

The role involves supporting public safety professionals through technical solutions, managing data analytics, and delivering training. It requires strong communication and organizational skills, alongside experience in data analysis.

The role involves analyzing data, creating data models, providing insights, building dashboards, and supporting marketing strategies through data-driven recommendations.

Lead RWD analysis strategy and execution: develop technical specifications and statistical programs, perform complex observational analyses (propensity scoring, ML), validate cohorts/variables, ensure QC, collaborate cross-functionally, and communicate results to non-technical stakeholders.

The Data Product Analyst designs and manages data products for insurance domains, ensuring they meet analytical and compliance standards while collaborating with cross-functional teams.

As a Senior Director Analyst, you will research data management solutions, provide insights, support sales efforts, and deliver high-value presentations.

Senior individual contributor who analyzes marketing performance across paid channels and CRM, owns GA4 tracking, partners with analytics engineers, builds dashboards, measures incrementality/LTV/CAC, and translates insights into actionable recommendations for stakeholders.

Lead high-complexity analytics projects: partner with stakeholders, prepare and blend large datasets, apply intermediate statistics, build models and visualizations, deliver insights, and mentor junior analysts.

Own the validation lifecycle: translate data quality requirements into clear deterministic rule specifications, maintain the validation catalogue, monitor and tune controls, perform first-pass anomaly detection and RCA support, and collaborate with Product, Technology, and Data Owners to ensure scalable, testable, and maintainable data quality controls.