At a Glance
- Tasks: Dive into data analysis, create impactful reports, and mentor junior analysts.
- Company: Join Made Tech, a forward-thinking company transforming public sector services.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on continuous improvement and innovation.
- Why this job: Make a real difference by shaping data strategies that improve society.
- Qualifications: Experience in data analysis, visualisation tools, and strong communication skills.
The predicted salary is between 28800 - 48000 £ per year.
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 will 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 will need to have a drive to deliver outcomes for users. You will make sure that the wider context of a delivery is considered and maintain alignment between the operational and analytical aspects of the engineering solution.
Key responsibilities
- 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 (desirable): Experience in automating data management activities to streamline processes and increase accuracy.
- 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.
- 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.
Senior Data Analyst in London employer: Made Tech
Made Tech is an exceptional employer that champions a flexible work culture, allowing you to balance your professional and personal life while making a meaningful impact in the public sector. With a strong focus on employee growth, we offer continuous development opportunities and a collaborative environment where your leadership skills can thrive. Join us to be part of a team dedicated to delivering excellence and enhancing service quality across the UK.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Analyst in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Made Tech!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data Analyst at Made Tech.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Made Tech.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Data Analyst at Made Tech, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Data Analyst in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Made Tech, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Made Tech. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Made Tech
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Made Tech!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
