Data Analyst in Manchester

Must be eligible to gain UK Security Clearance. Made Tech wants to positively impact the country's future by using technology to improve society, for everyone. We want to empower the public sector to deliver and continuously improve digital services that are user‑centric, data‑driven and freed from legacy technology. A key component of this is developing modern data systems and platforms that drive informed decision‑making for our clients. You will also work closely with clients to help shape their data strategy.

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.

You’ll need to have a drive to deliver outcomes for users. You’ll make sure that the wider context of a delivery is considered and maintain alignment between the operational and analytical aspects of the engineering solution.

Skills, Knowledge & Expertise

• Analysis and synthesis: 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.
• Data Management: 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 modelling, cleansing, and enrichment: 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, Validation, and Linkage: 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.
• Statistical Methods and Data Analysis: 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.
• 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.
• Logical and creative thinking: 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.

Benefits

30 days Holiday – we offer 30 days of paid annual leave plus