Data Analyst (Tableau)

Harrogate
9 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst Training Course (Excel, SQL & Power BI)

Data Analyst Training Course (Excel, SQL & Power BI)

Technical Data Engineer / Analyst

Data Engineer

Data Engineer

Data Engineer

I'm partnered with a PLC who are looking for a Data Analyst to join the team initially on a 12-month contract with potential to be made permanent.

This role will report to the Group Data Analytics Manager and will be responsible for delivering data workflows, visualisations and generating business insights. This will provide a foundation on which the business can make quicker, more informed business decisions. You will work collaboratively with stakeholders to aid in decision making and be a trusted, respected & knowledgeable point of contact between the Group Data Analytics team and the business.

Responsibilities:

Analyse large and complex datasets and present these insights clearly to end users, for enhanced decision making.
Working closely with business stakeholders to understand their analytical needs and requirements, including explaining complex principles to stakeholders of varying technical backgrounds.
Sharing knowledge, collaborating and supporting other team members to assist them with their projects and development.
Supporting business super users with their technical queries including delivering training and knowledge sharing.
Design, develop, and maintain interactive and visually appealing dashboards using Tableau, sticking to the group's style guide where appropriate, whilst always looking for ways to enhance analysis and functionality where possible.
Ensure dashboards are user-friendly, automated and provide actionable insights for stakeholders.
Ensure all dashboards follow a strict peer review, QA and UAT process.
Utilize Alteryx to create ETL pipelines, ensuring data accuracy and completeness throughout the process.
Ensure all workflows follow a strict peer review, QA and UAT process.
Utilise data services, whether that be dashboards, ETL pipelines, or curated data sources to generate new business insights.
Business partner with divisional stakeholders to present these insights and advise on how these can enhance decision making.
Link these insights to tangible actions that can aid the business in increasing revenue and reducing cost Skillset required:

Data visualisation experience with Tableau or similar product. Proficient in creating dashboards using multiple datasources and layouts.
Excellent communication skills, including being able to adapt your communication style to suit the required audience. Also having the ability to influence others around you in the team.
Strong attention to detail with exceptional analytical skills, including the ability to interpret complex business processes.
Knowledge of SQL, Python, R or similar programming languages is advantageousIf this role is of interest, please apply directly with an updated CV

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.

Careers

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

Jobs

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

Education

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

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.