Proficiency in applying analytical methods such as statistical analysis, data mining, and qualitative analysis.
Experience synthesizing research data to present actionable insights and solutions.
Ability to articulate the impact of analysis on decision-making and problem-solving.
Ability to engage skeptical colleagues and gain buy-in.
Familiarity with common data sources and general knowledge of data organization and storage practices.
Understanding of data governance standards and commitment to following team data quality practices.
Ability to contribute to improvements in data management practices through documentation, team training, and discussions.
Experience using data management tools, with willingness to learn more about maintaining efficiency and integration.
Basic understanding of data governance policies, including data security and ethical standards.
Experience with conceptual, logical, and physical data modeling and ability to follow data modeling standards and best practices.
Experience resolving data quality issues and ensuring data accuracy through cleansing and standardization techniques.
Basic experience with ETL tools for data integration and storage, with a focus on learning to ensure interoperability with other datasets.
Experience working with other data professionals to improve data modeling and integration practices.
Experience with data profiling, validation checks, and multi-source data linkage.
Ability to prepare datasets by managing missing values, duplicates, and advanced cleansing.
Ability to communicate data limitations to stakeholders.
Ability to apply statistical techniques including hypothesis testing, regression analysis, and clustering.
Ability to analyze data using programming languages or software and share insights with technical and non-technical audiences.
Ability to manage expectations and interact across technical and business stakeholder groups.
Ability to translate basic business requirements into technical solutions.
Ability to simplify complex data insights into clear presentations for varied audiences.
Ability to break down problems logically, generate structured solutions, prioritize tasks, and resolve issues efficiently.
Ability to maintain alignment between operational and analytical aspects of engineering solutions.
Must hold live SC clearance due to client requirements.
Responsibilities
Conduct in-depth data analysis, generate reports, and provide actionable insights for client projects.
Produce business intelligence dashboards using industry-standard tools such as Power BI, Tableau, and QuickSight.
Collaborate with clients to understand their needs, translate them into analytical solutions, and present findings clearly and actionably.
Consider the wider delivery context and maintain alignment between operational and analytical aspects of the engineering solution.
Apply statistical, qualitative, and data mining techniques tailored to research contexts.
Synthesize data to deliver actionable insights and articulate impacts on decision-making.
Maintain data accuracy, accessibility, and storage using common data sources.
Adhere to team data governance, security, and ethical standards.
Use 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.
Use ETL tools to support data interoperability across datasets.
Collaborate with data professionals to refine data modeling and integration practices.
Create visual representations tailored to audience requirements.
Apply visualization tools such as Tableau, Power BI, Matplotlib, and Seaborn.
Follow core design and accessibility principles to produce clear, accurate visuals.
Incorporate peer feedback to refine visualization quality.
Prepare datasets by managing missing values, duplicates, and advanced cleansing.
Participate in peer reviews to uphold data accuracy standards.
Execute statistical techniques including hypothesis testing, regression analysis, and clustering.
Explore and apply emerging statistical methodologies to real-world problems.
Maintain active updates, respond to inquiries, and foster collaborative environments.
Desired Qualifications
Support continuous improvement, documentation, and process automation.
Take an interest in learning to automate data management activities to streamline processes and improve accuracy.
About Made Tech Our aim at Made Tech is to use human-centred technology to improve our society. We believe putting people at the heart of designing, building and delivering public services leads to better outcomes 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
About the role As a Data Analyst, you may play one or more roles according to our clients' needs. The role is very hands-on and you'll support as contributor for a project, focusing on:
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.
Analysis & Synthesis
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.
Data Management
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.
Data Modeling, Cleansing & Enrichment
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.
Data Visualization
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.
Quality Assurance, Validation & Linkage
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.
Statistical Methods & Data Analysis
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.
Business Skills
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.
Logical & Creative Thinking
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.
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.
At this point, we hope you're feeling excited about Made Tech and the job opportunity. Get in touch with our talent team if you’d like an informal chat about the role and your suitability before applying. We are hiring for this role directly, so will not respond to any CVs sent via external recruitment agencies.
SC clearance must be held and live due to our client requirements.
Support in applying
If you need this job description in another format, or other support in applying, please email [email protected].
We believe we can use tech to make public services better. We also believe this can happen best when our own team represents the society that actually uses the services we work on. We’re collectively continuing to grow a culture that is happy, healthy, safe and inspiring for people of all backgrounds and experiences, so we encourage people from underrepresented groups to apply for roles with us.
When you apply, we’ll put you in touch with a member of our talent team who can help with any needs or adjustments we may need to make to help with your application. We’ve put together this blog as a resource to share more about reasonable adjustments and some examples of what this could include. We also welcome any feedback on how we can improve the experience for future candidates.
Life at Made Tech
We’re committed to building a happy, inclusive and diverse workforce. You can get a sense of what it’s like working here from our blog, where we talk about mental health, communities of practice and neurodiversity as well as our client work and best practice.
Like many organisations, we use Slack to chat to each other. The Slack groups that have formed give an idea of the diversity within Made Tech. If you’d like to speak to someone from one of these groups about their experience as an employee, please let one of the Made Tech Talent Team know.