Data Analyst

Department: Technology

Employment Type: Permanent

Location: Any UK Office Hub (Bristol / London / Manchester / Swansea)

Compensation: £37,000 - £45,000 / year

Description

• Data analysis and reporting: Conducting in-depth data analysis, generating reports, and providing actionable insights for client projects. 
• Data and BI visualisation: Producing BI dashboards using industry-standard tools - Power BI, Tableau, Quicksight etc 
• Client interaction: Collaborating with clients to understand their needs, translating these into analytical solutions, and presenting findings in a clear, actionable manner. 

Key Responsibilities

• Apply statistical, qualitative, and data mining techniques tailored to research contexts.
• Synthesize data to deliver actionable insights and articulate impacts on decision-making.
• Engage effectively with skeptical colleagues to build consensus and buy-in.

• Maintain data accuracy, accessibility, and storage using common data sources.
• Adhere to team data governance, security, and ethical standards.
• Support continuous improvement, documentation, and process automation (desirable).
• Utilize and learn data management tools to maintain integration and efficiency.

• Design conceptual, logical, and physical data models using best practices.
• Perform data cleansing and standardization to resolve quality issues.
• Gain exposure to ETL tools to ensure data interoperability across datasets.
• Collaborate with data professionals to refine modeling and integration practices.

• Create visually appealing representations tailored to audience requirements.
• Apply visualization tools (e.g. Tableau, Power BI, Matplotlib, Seaborn).
• Follow core design and accessibility principles to produce clear, accurate visuals.
• Incorporate peer feedback to refine visualization quality.

• Conduct data profiling, validation checks, and multi-source data linkage.
• Prepare datasets by managing missing values, duplicates, and advanced cleansing.
• Communicate data limitations to assist stakeholders in informed decision-making.
• Participate in peer reviews to uphold data accuracy standards.

• Execute statistical techniques including hypothesis testing, regression analysis, and clustering.
• Analyze data via programming languages/software to share insights with technical and non-technical audiences.
• Explore and apply emerging statistical methodologies to real-world problems.

• Manage expectations and interact across technical and business stakeholder groups.
• Maintain active updates, respond to inquiries, and foster collaborative environments.
• Translate basic business requirements into technical solutions.
• Simplify complex data insights into clear presentations for various audiences.

• Break down problems logically and generate structured solutions.
• Make informed decisions, prioritize tasks, and resolve issues efficiently.
• Demonstrate adaptability, curiosity, and a strong continuous learning orientation.

Skills, Knowledge & Expertise

• Proficiency in applying various analytical methods such as statistical analysis, data mining, and qualitative analysis.
• Experience in synthesising research data to present actionable insights and solutions.
• Ability to articulate the impact of their analysis on decision-making and problem-solving.
• Effective communication skills to engage and gain buy-in from sceptical colleagues.
• Familiarity with common data sources and general knowledge of data organisation and storage practices. 
• Understanding of data governance standards and a commitment to following data quality practices set by the team.
• Ability to contribute to improvements in data management practices by supporting documentation, learning from team training, and actively participating in discussions
• Experience with using data management tools, with a willingness to learn more about maintaining efficiency and integration.
• Basic understanding of data governance policies, with a focus on following data security and ethical standards.
• An interest in learning how to automate data management activities to streamline processes and improve accuracy (desirable).
• Experience with conceptual, logical, and physical data modelling. Ability to adhere to data modelling standards and best practices.
• Experience in resolving data quality issues and ensuring data accuracy through cleansing and standardisation techniques.
• Basic experience with ETL tools for data integration and storage, with a focus on learning how to ensure data interoperability with other datasets. 
• Some experience working with other data professionals, with a focus on learning and improving data modelling and integration practices through teamwork. 

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Job Benefits