Senior Data Analyst

• 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.
• Mentoring junior analysts, leading data-focused projects, and setting best practices in data analysis 

Technical Skills

Analysis and synthesis

• 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 and persuasion skills to engage and gain buy-in from sceptical colleagues. Ability to foster collaboration and address concerns to ensure adherence to best practices.
• Advisory and critique skills: Capability to advise on the choice and application of analytical techniques and critique colleagues' findings to ensure high standards in data analysis.

 Data Management

• Understanding of data sources and storage: Knowledge of various data sources, data organisation, and storage practices. Commitment to maintaining data integrity and accessibility.
• Advocacy for data governance: Experience in advocating for data governance standards and influencing team adherence to data quality practices.
• Continuous improvement: Ability to communicate and implement continuous improvements in data management practices through documentation, training, and regular team engagement.
• Toolset management: Proficiency in defining and supporting common toolsets for data management, ensuring efficiency and seamless integration.
• Automation of data management: Experience in automating data management activities to streamline processes and increase accuracy. (desirable)
• Compliance with data governance policies:  Understanding and ensuring compliance with data governance policies, maintaining data security and ethical standards.

Data modelling, cleansing, and enrichment

• Data modelling expertise: Proficient in conceptual, logical, and physical data modelling. Ability to adhere to data modelling standards and best practices.
• Data cleansing and standardisation: Experience in resolving data quality issues and ensuring data accuracy through cleansing and standardisation techniques.
• Use of data integration tools: Skilled in using ETL tools for data integration and storage. Ensures data interoperability with other datasets.
• Collaboration with data professionals: Experience collaborating with other data professionals to improve modelling and integration standards and patterns.

Data Visualisation

• Interpretation of requirements: Ability to interpret data visualisation requirements and create meaningful, visually appealing representations tailored to the audience.
• Proficiency in visualisation tools: Experience with tools such as Tableau, Power BI, and Python libraries like Matplotlib and Seaborn. Knowledge of selecting appropriate visualisation types.
• Application of visualisation standards: Application of design principles to create clear, accurate, and accessible visualisations. Awareness of accessibility considerations.
• Mentorship in visualisation: Experience in reviewing and advising junior members to improve the quality and efficiency of data visualisations.

Data Quality Assurance, Validation, and Linkage

• Data quality assurance: Experience in implementing processes for data quality assessment and improvement, including data profiling, cleansing, and standardisation.
• Data validation and linkage: Ability to perform data validation checks and integrate data from various sources to ensure consistency and accuracy.
• Data cleansing and preparation: Proficiency in defining data cleansing processes and preparing data for analysis by handling missing values, outliers, and duplicates.
• Communication of data limitations:  Skilled in articulating data constraints and limitations to stakeholders, providing context for informed decision-making.
• Peer review and quality control: Experience in conducting peer reviews to validate data outputs, ensuring high standards of accuracy and reliability.

Statistical Methods and Data Analysis

• Knowledge of statistical methodologies: Proficient in various statistical methods, such as hypothesis testing, regression analysis, clustering, and time series analysis. Ability to select appropriate techniques based on project requirements.
• Data analysis and interpretation: Experience in using statistical software or programming languages to perform data analysis and generate insights. Skilled in communicating findings to technical and non-technical stakeholders.
• Application of emerging theory: Willingness to explore and apply new statistical methodologies or practices to solve practical problems and adapt to emerging theories.

Business Skills

Communication

• Stakeholder communication: Experience in effectively engaging with a diverse range of stakeholders, including technical and business individuals. Ability to manage expectations and facilitate productive discussions.
• Active and reactive communication: Proficiency in handling both proactive communication (updates, insights) and reactive communication (responding to inquiries, addressing concerns) to maintain a collaborative atmosphere.
• Interpretation of stakeholder needs: Ability to understand and translate stakeholder requirements into technical solutions. Experience in bridging the gap between technical and non-technical stakeholders.
• Presentation and sharing of insights: Skilled in presenting complex data in a clear, understandable manner tailored to diverse audiences, including senior management.

Logical and creative thinking

• Problem-solving approach:  Ability to apply logical and creative thinking to resolve complex problems by breaking them down and generating innovative solutions.
• Decision-making and action-taking: Skilled in making informed decisions, prioritising tasks, and taking appropriate actions to resolve issues efficiently.
• Adaptability and learning orientation: Demonstrates adaptability in strategies and a commitment to continuous learning and improvement.
• Interpretation of stakeholder needs: Ability to understand and translate stakeholder requirements into technical solutions. Experience in bridging the gap between technical and non-technical stakeholders.
• Presentation and sharing of insights: Skilled in presenting complex data in a clear, understandable manner tailored to diverse audiences, including senior management.

Life at Made Tech

• antiracist-activists
• disability
• lgbtqiaplus-allies-and-activists
• neurodiversity
• parents-carers
• women-in-tech