Senior Data Analyst

Made Tech
Bristol
1 week ago
Create job alert

This job is brought to you by Jobs/Redefined, the UK's leading over-50s age inclusive jobs board.


Senior Data Analyst

Department: Technology


Employment Type: Permanent


Location: Any UK Office Hub (Bristol / London / Manchester / Swansea)


Compensation: GBP 49,500 - GBP 65,000 / year


Description

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.


As a Senior 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 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.

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.


Technical Skills

  • 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.
  • 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 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.
  • 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: 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.
  • 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

  • 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.
  • 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.

Presentation and sharing of insights: Skilled in presenting complex data in a clear, understandable manner tailored to diverse audiences, including senior management.


At this point, we hope you're feeling excited about Made Tech and the job opportunity. Even if you don't feel that you meet every single requirement, we still encourage you to apply. 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. An increasing number of our customers are specifying a minimum of SC (security check) clearance in order to work on their projects. As a result, we're looking for all successful candidates for this role to have eligibility. Eligibility for SC requires 5 years' continuous UK residency and 5 year' employment history (or back to full‑time education). Please note that if at any point during the interview process it is apparent that you may not be eligible for SC, we won't be able to progress your application and we will contact you to let you know why.


Job Benefits

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.


Here are some of our most popular benefits listed below:



  • 30 days Holiday - we offer 30 days of paid annual leave plus bank holidays
  • Flexible Working Hours - we are flexible with what hours you work
  • 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


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst (12 Month Contract)

Senior Data Analyst

Senior Data Analyst

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.