Data Analyst
Descrizione del posto
Texte original importé depuis Reed
Robert Half is excited to be exclusively partnering with a well-established organisation in Shrewsbury to recruit for a newly created Data and AI Analyst role. This permanent position offers a salary of up to £40,000 and hybrid working, with 2-3 days per week in the Tetbury office.
This is a key role supporting the business during a pivotal stage of its digital modernisation journey. You will be responsible for enabling commercial and operational decision-making through insightful data analysis and reporting. The successful candidate will also play a central role in helping the business adopt AI solutions and implement best practices around AI.
Key Responsibilities:
- Produce effective data analysis of sales, aftersales and service performance
- Identify data trends, risks and opportunities
- Develop and maintain dashboards using Power BI, Excel
- Support build of ad-hoc reports, partner with stakeholders to understand reporting requirements
- Provide accurate commercial insights
- Automate reports using Python
About You:
- Experience in Data Analysis
- Strong Power BI, SQL and Excel skills
- Ability to interpret data, provide actionable insights
- Passion for AI and some Python experience would be beneficial
- Ability to communicate with different stakeholders
On Offer
Salary up to £40,000, plus hybrid working (2-3 days a week in the Shrewsbury office) plus competitive benefits!
Robert Half Ltd acts as an employment business for temporary positions and an employment agency for permanent positions. Robert Half is committed to diversity, equity and inclusion. Suitable candidates with equivalent qualifications and more or less experience can apply. Rates of pay and salary ranges are dependent upon your experience, qualifications and training. If you wish to apply, please read our Privacy Notice describing how we may process, disclose and store your personal data:
Competenze chiave estratte
La nostra IA ha analizzato l'offerta per identificare le competenze richieste.
I nostri consigli per candidarsi
5 recommandations générées par notre IA pour maximiser vos chances.
⭐ Lead your CV Personal Statement with Power BI and SQL as the advert lists these as the primary technical requirements under 'About You'.
📊 Quantify your data analysis impact: e.g. 'Built 5 Power BI dashboards tracking £2M monthly sales pipeline, reducing reporting time by 40%'.
🤖 Include a dedicated line on AI tools or projects — even exploratory ones — as the advert explicitly seeks a 'passion for AI' and involvement in AI adoption.
🐍 Highlight any Python scripting or automation work, even if secondary to your main role, as Python experience is called out as beneficial for report automation.
🎯 Tailor your experience to commercial and operational contexts (sales, aftersales, service) to match the specific business performance areas named in the responsibilities.
Bullets CV suggérés
3 bullets générés par notre IA pour cette offre, alignés sur ses mots-clés ATS.
Ajoutez ces 3 bullets sous votre expérience la plus récente :
- •Designed and maintained 8 Power BI dashboards tracking sales and aftersales KPIs across 3 business units, reducing manual reporting effort by 35% within 6 months.
- •Automated 12 weekly commercial reports using Python (pandas, openpyxl), cutting generation time from 4 hours to under 20 minutes and eliminating recurring data entry errors.
- •Partnered with 6 senior stakeholders to define reporting requirements, delivering SQL-based ad-hoc analyses that informed a £500k pricing strategy review.
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Aperçu — adapté à Robert Half
Dear Hiring Manager,
Robert Half's newly created Data and AI Analyst position at your Shrewsbury organisation stands out precisely because it combines rigorous commercial data analysis with hands-on AI adoption — two areas where I have built focused expertise. My experience with Power BI dashboard development and SQL-driven reporting directly aligns with the sales, aftersales and service performance analysis described in the role.
My background in data analysis spans building automated reporting pipelines using Python and designing Power BI dashboards that distil complex datasets into clear commercial insights for senior stakeholders. I am comfortable partnering with non-technical teams to understand their reporting requirements and translating those into scalable, maintainable solutions. I have also begun exploring practical AI tools to streamline analytical workflows, which mirrors the AI best-practice remit outlined in this role.
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Domande probabili del colloquio
10 questions générées à partir de cette offre.
Tecniche
- ›Walk us through how you would design a Power BI dashboard to track sales and aftersales performance for a non-technical stakeholder.
- ›How have you used SQL to extract and transform data for a reporting or analysis project?
- ›Describe a time you automated a report using Python — what libraries did you use and what was the outcome?
- ›What approach do you take to identify trends and anomalies in large commercial datasets?
- ›How would you evaluate and implement an AI solution within a business that is early in its digital modernisation journey?
Comportamentali
- ›Tell me about a time you had to translate complex data findings into actionable recommendations for a non-technical audience.
- ›Describe a situation where you identified a significant data trend or risk that influenced a business decision.
- ›Give an example of when you managed competing priorities from multiple stakeholders requesting different reports or insights.
- ›Tell me about a time you proactively improved a reporting process — what drove the change and what was the result?
- ›Describe a situation where you had to learn a new tool or technology quickly to deliver a project on time.
Exemples de réponses STAR
Réponses modèles avec la méthode Situation-Tâche-Action-Résultat. À adapter à votre vécu.
Tell me about a time you had to translate complex data findings into actionable recommendations for a non-technical audience.
Describe a situation where you proactively improved a reporting process — what drove the change and what was the result?