At a Glance
- Tasks: Identify and analyse data requirements, ensuring clarity and alignment with business objectives.
- Company: Join a forward-thinking company that values collaboration and innovation.
- Benefits: Enjoy competitive pay, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with excellent career advancement opportunities.
- Why this job: Make a real impact by shaping data solutions that drive business success.
- Qualifications: Experience in data analysis and strong stakeholder management skills required.
The predicted salary is between 55000 - 65000 £ per year.
The primary role is to provide Senior Data Business Analyst deliverables to the data team by identifying, analysing, and documenting data-related requirements across projects and BAU activities. This includes working closely with the Data Architect, Data Analysts, and business stakeholders to ensure requirements are clearly defined, understood, and delivered effectively. Responsibilities include:
- Mapping “as is” and “to be” processes, focusing on data flows, data usage, and reporting outputs.
- Ensuring proposed solutions are efficient, scalable, and aligned with agreed data architecture standards and process management standards.
- Documenting data and reporting requirements in conjunction with the business, Data Architect, Data Analysts, and Project Manager.
- Translating business needs into clear functional specifications, data definitions, and business rules.
- Supporting the production of project documentation, including requirements, process flows, and data mappings.
- Prioritising and delivering data changes.
- Running requirements workshops with internal stakeholders and third-party suppliers where required.
- Maintaining documentation in accordance with the firm’s project methodology, data governance standards, and process management standards.
- Liaising with stakeholders to ensure they understand the benefits, limitations, and impacts of data and reporting changes.
- Communicating to users regarding data changes and how they affect downstream processes and reporting.
- Working with the business and Learning & Development to support user documentation, definitions, and training where required.
- Ensuring appropriate handover of data solutions into BAU support, including documentation and agreed ownership.
- Assisting with the development of UAT documentation, test scenarios, and tracking of outcomes.
- Validating outputs from data processes, reports, and dashboards against documented requirements.
- Supporting data quality initiatives, including identification and resolution of inconsistencies or gaps.
- Contributing to the development and maintenance of data definitions, standards, and governance practices.
You: Experienced practitioner with relevant qualifications, likely to have a relevant degree or equivalent and may be a part qualified or qualified professional within a discipline or have equivalent experience in a data or analytical environment. Experience includes:
- Working as a Business Analyst, ideally within a data, reporting, or analytics-focused team.
- Interpreting business requirements and translating them into data and technical specifications.
- Working with data teams, including Data Architects, Data Analysts, or Data Engineers.
- Strong understanding of data concepts, including data structures, data flows, and data quality considerations.
- Supervising colleagues, providing guidance, and ensuring adherence to agreed standards.
- Analysing and interpreting complex information and understanding impacts across systems, data, and reporting.
- Strong stakeholder management skills, with the ability to influence and build credible relationships across the firm.
- Excellent verbal and written communication skills, with the ability to communicate effectively with both technical and non-technical audiences.
- Facilitating workshops and presenting complex information in a clear and structured way.
- High levels of attention to detail, particularly around data definitions and requirements accuracy.
- Team-focused mentality, working collaboratively to deliver successful outcomes.
- Strong analytical and problem-solving skills.
- Able to use judgement and experience to resolve issues and escape where appropriate.
- Motivated, proactive, and able to manage multiple priorities.
- Exposure to Power BI, SQL, Fabric, Purview, and other enterprise data tools would be beneficial.
Senior Data Business Analyst employer: Saffery Champness
As a Senior Data Business Analyst at our company, you will thrive in a dynamic and collaborative work environment that prioritises innovation and professional growth. We offer comprehensive training programmes, competitive benefits, and a culture that values teamwork and open communication, ensuring that you are well-equipped to excel in your role. Located in a vibrant area, our office provides a stimulating atmosphere that fosters creativity and engagement, making it an excellent place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Business Analyst
✨Get Involved in Data Science Meetups
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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 Saffery Champness.
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We think you need these skills to ace Senior Data Business Analyst
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 Saffery Champness, 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 Saffery Champness. 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 Saffery Champness
✨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 Saffery Champness!
✨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.
