Data Analyst

Key Responsibilities

• 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.

Skills, Knowledge & Expertise

• Application of analytical techniques: Proficiency in applying various analytical methods such as statistical analysis, data mining, and qualitative analysis. Ability to select and apply appropriate techniques based on the context and research data.
• Synthesis of research data: 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.
• Engagement with sceptical colleagues: Effective communication skills to engage and gain buy-in from sceptical colleagues. 
  
  

• Good understanding of data sources and storage: Familiarity with common data sources and general knowledge of data organisation and storage practices. Willingness to maintain data accuracy and accessibility.
• Awareness of data governance: Understanding of data governance standards and a commitment to following data quality practices set by the team.
• Continuous improvement: Ability to contribute to improvements in data management practices by supporting documentation, learning from team training, and actively participating in discussions.
• Toolset support: Experience with using data management tools, with a willingness to learn more about maintaining efficiency and integration.
• Interest in automation: An interest in learning how to automate data management activities to streamline processes and improve accuracy (desirable).
• Compliance with data governance policies: Basic understanding of data governance policies, with a focus on following data security and ethical standards.

• Data cleansing and standardisation: Experience in resolving data quality issues and ensuring data accuracy through cleansing and standardisation techniques.
• Exposure to data integration tools: Basic experience with ETL tools for data integration and storage, with a focus on learning how to ensure data interoperability with other datasets.
• Collaboration with data professionals: Some experience working with other data professionals, with a focus on learning and improving data modelling and integration practices through teamwork.

• Understanding visualisation requirements: Ability to understand data visualisation needs and create simple, visually appealing representations suited to the audience.
• Good working knowledge of visualisation tools: Experience using tools like Tableau, Power BI, or Python libraries (e.g., Matplotlib, Seaborn), with a willingness to learn how to choose the right visualisation type for different data sets.
• Awareness of visualisation standards: Understanding of design principles to create clear and accurate visualisations, with an interest in learning about accessibility best practices.
• Willingness to learn from peers: Open to feedback and guidance from senior team members to improve the quality of your visualisations.
  
  

• Data quality assurance: Familiarity with data quality assessment techniques, such as data profiling and cleansing, with a willingness to learn more about improving data accuracy and consistency.
• Data validation and linkage: Experience performing basic data validation checks and combining data from different sources, with guidance from senior team members.
• Data cleansing and preparation: Experience in data preparation, including handling missing values and duplicates, with a focus on learning more advanced data cleansing techniques.
• Communication of data limitations: Ability to discuss data limitations with guidance from others, helping stakeholders understand potential issues and make informed decisions.
• Participating in peer reviews: Willingness to participate in peer reviews to improve data accuracy, with the support of more experienced team members.
  
  

• Knowledge of statistical methods: Familiarity with common statistical techniques like hypothesis testing, regression analysis, and basic clustering, with an eagerness to learn how to choose the right methods for different projects.
• Data analysis and interpretation: Experience using statistical software or programming languages for data analysis, with guidance in generating insights and sharing findings with both technical and non-technical audiences.
• Willingness to learn new methodologies: Interest in exploring and applying new statistical techniques, with support from senior team members, to solve real-world problems and stay updated on emerging theories.
  
  

• Stakeholder communication: Some experience working with different types of stakeholders, both technical and business-focused, with a focus on learning to manage expectations and contribute to productive discussions.
• Willingness to engage in active and reactive communication: Comfortable sharing updates and responding to inquiries, with support from team members, to help maintain a collaborative working environment.
• Interpretation of stakeholder needs: Ability to understand basic stakeholder requirements and help translate them into technical solutions, with guidance in bridging the gap between technical and non-technical individuals.
• Presentation skills: Experience presenting data and insights, with a focus on learning how to simplify complex information for various audiences, including senior team members.
  
  

• Problem-solving skills: Ability to apply logical thinking to break down simpler problems and contribute to generating solutions, with support from more experienced team members.
• Decision-making and action-taking: Experience in making informed decisions and prioritising tasks, with guidance to take appropriate actions in resolving issues efficiently.
• Adaptability and learning orientation: Willingness to adapt to new challenges and a strong desire to learn and improve continuously.