At a Glance
- Tasks: Analyse data to uncover insights and support decision-making with actionable recommendations.
- Company: Join a forward-thinking company that values collaboration and innovation.
- Benefits: Enjoy a hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Dynamic role with excellent career advancement opportunities in a fast-paced environment.
- Why this job: Make a real impact by transforming data into valuable insights for the business.
- Qualifications: Strong analytical skills, SQL proficiency, and experience with data visualisation tools required.
The predicted salary is between 45000 - 55000 £ per year.
- Role
- Data Analyst/Data Modelling Analyst
- Hybrid working model with 3 days work from office
- Permanent/ Fixed term/ Contract inside IR 35
- Contract Length: Initial 3 months-6 months
- Immediate start
Applicants must be legally authorized to work in the United Kingdom without the need for current or future visa sponsorship
Responsibilities
- Data Analysis & Insight Generation
- Analyse business and operational data to identify trends, insights, risks and opportunities.
- Develop clear and actionable recommendations based on data analysis.
- Support the creation and maintenance of dashboards, reports and datasets that enable informed decision-making.
- Define, track and report on key performance indicators and success metrics.
- Work with stakeholders to understand data requirements and translate them into meaningful analytical outputs.
- Data Quality & Governance
- Ensure data quality and integrity across the full data lifecycle, from source systems through to reporting and analytics.
- Conduct data validation, reconciliation and quality assurance activities to identify and resolve data issues.
- Support the definition and implementation of data quality rules and controls.
- Assist with documenting data lineage, transformations and business definitions to support transparency and trust in data.
- Promote best practices for data governance, accuracy and consistency.
- Data Modelling & Architecture Support
- Support the design, development and maintenance of logical and physical data models across business domains.
- Assist with dimensional modelling activities, including fact and dimension design using Kimball methodologies.
- Support the documentation and maintenance of Data Vault models within raw and business integration layers.
- Help define and document data flows, business entities and relationships across enterprise systems.
- Contribute to the ongoing development of architecture standards, principles and modelling best practices.
- Support metadata management, data catalogue and data discoverability initiatives.
Requirements & Business Engagement
- Engage with business stakeholders to understand requirements, pain points and opportunities.
- Translate business requirements into clear and technically viable data specifications.
- Collaborate with Engineers, Architects and Product teams to ensure solutions meet business needs.
- Support workshops, discovery activities and requirement gathering sessions.
- Maintain clear traceability between user requirements and delivered data products.
- Collaboration & Communication
- Work closely with Data Architects, Data Engineers, Product Managers and Business stakeholders to deliver effective data solutions.
- Produce clear documentation, presentations and recommendations suitable for technical and non-technical audiences.
- Communicate findings, insights and proposed solutions in a concise and engaging manner.
- Support adoption and understanding of data products through training, documentation and stakeholder engagement.
Skills & Experience
Essential
- Strong analytical skills with the ability to interpret complex datasets and translate findings into actionable insights.
- Proficiency in SQL with experience querying, validating and analysing data directly from databases.
- Experience working with reporting tools, dashboards, performance metrics and analytical datasets.
- Understanding of data quality principles, validation techniques and governance processes.
- Ability to gather and document business requirements and translate them into technical solutions.
- Strong problem-solving and critical-thinking capabilities.
- Excellent written and verbal communication skills.
- Experience using Microsoft Excel and data visualisation tools such as Power BI.
- Ability to manage multiple tasks and priorities in a fast-paced environment.
Desirable
- Understanding of dimensional modelling techniques, including fact and dimension modelling.
- Exposure to Kimball methodology and data warehousing concepts.
- Understanding of Data Vault principles and modern data architecture practices.
- Experience documenting data lineage, metadata and business definitions.
- Familiarity with cloud-based data platforms and analytics ecosystems.
- Additional Skills
- Understanding of data governance, master data and enterprise data management concepts.
- Experience working in Agile delivery environments.
- Exposure to data catalogue or metadata management tools.
- Experience using Databricks for querying, analysing and transforming data within cloud-based analytics environments.
- Key Technical Skills
Data Analysis, Data Modeling, Power BI, SQL, Cloud Data Warehousing, Dashboard Development, Metadata Management, Databricks, Data Vault, Data Validation, Microsoft Excel
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Data Analyst/Data Modelling Analyst(Lead II - Data Engineering) in Leeds employer: UST
As a Principal Data Engineer at our company, you will thrive in a dynamic and innovative work culture that prioritises technical excellence and continuous learning. With opportunities for mentorship and collaboration across diverse teams, you will play a pivotal role in shaping the future of our cloud-scale data platform while enjoying the flexibility of a hybrid work environment in Nottingham or London. We are committed to fostering your professional growth and providing a supportive atmosphere where your contributions directly impact our success.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst/Data Modelling Analyst(Lead II - Data Engineering) in Leeds
✨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 UST!
✨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 Data Analyst/Data Modelling Analyst(Lead II - Data Engineering) at UST.
✨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 UST.
✨Apply Directly through Our Website
When you find a suitable opening like Data Analyst/Data Modelling Analyst(Lead II - Data Engineering) at UST, 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 Data Analyst/Data Modelling Analyst(Lead II - Data Engineering) in Leeds
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 UST, 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 UST. 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 UST
✨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 UST!
✨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.
