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Open nowFirst seen 6 hours ago

Senior Data Analyst

made-tech30 open roles

Pay
£49,000 – £60,000 a year
Where
l
Work mode
Hybrid
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Your applicationOpen nowSenior Data Analystmade-tech · l

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: first seen 6 hours ago

The posting

As a Senior Data Analyst at Made Tech, you’ll play a pivotal role in helping public sector organisations become truly data-led. You’ll join our data team in its mission to get data knowledge and skills out of silos and embedded into delivery teams. You will provide advanced data modelling, predictive analytics, and data visualisation, allowing us to deliver more sophisticated and customised solutions to our clients.

The role is very hands-on and you'll support as a senior contributor role 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.
  • 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. Must have experience with SQL
  • 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

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, let your recruitment agent or Made Tech Talent Partner know.

The groups are:

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

We are always listening to our growing teams and evolving the benefits available to our people. As we scale, as do our benefits and we are scaling quickly. We've recently introduced a flexible benefit platform which includes a Smart Tech scheme, Cycle to work scheme, and an individual benefits allowance which you can invest in a Health care cash plan or Pension plan. We’re also big on connection and have an optional social and wellbeing calendar of events for all employees to join should they choose to.

Here are some of our most popular benefits listed below:

✈️ 30 days Holiday - we offer 30 days of paid annual leave

👶 Flexible Parental Leave - we offer flexible parental leave options

👩‍💻 Remote Working - we offer part time remote working for all our staff

🤗 Paid counselling - we offer paid counselling as well as financial and legal advice.

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