Data and Reporting Analyst

Leeds
10 months ago
Applications closed

Related Jobs

View all jobs

Data Governance Analyst (PIM)

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

Data Governance Analyst

Data Governance Analyst

Data Governance Analyst

Data Engineer | Outside IR35 | £400 - £500 | 6 months | Hybrid Nottingham

Data and Reporting Analyst
-Leeds/Hybrid
-Up to £50k

A digital marketing agency in Leeds are seeking a strategic and technically skilled Data and Reporting Analyst to establish a data and analytics team.
As a Data and Reporting Analyst you will be responsible for creating and implementing tracking requirements, then using the advanced tracking implemented, build out customised reporting dashboards to showcase performance against a client's objectives and KPIs. The Data and Reporting Analyst will work across a wide range of clients, supporting multiple teams

The role:

  • Lead projects using data modelling and analysis techniques to guide strategy.
  • Support to build the team's goals, roadmaps and processes.
  • Implement campaign tracking through Google Tag Manager.
  • Build custom reporting dashboards using tools such as Looker Studio and Power BI.
  • Analyse data to provide actionable insights that enhance campaign performance.
  • Develop and maintain the reporting dashboard to visualise key metrics for clients and internal teams. (Power BI, Tableau, Looker Studio)
  • Identify opportunities to integrate data from multiple platforms using existing tools such as Adverity or Google Big Query and suggest news ways of combining data.
  • Implement cookie compliance across new websites and existing websites, creating a reusable and efficient solution.
  • Feed into new business decks, providing top level insights to initiate new business opportunities.
    Skills required:
  • Strong experience with GA4 and Google Tag Manager.
  • Strong working knowledge of Looker Studio and Power BI.
  • Hands-on experience with data pipelines and ETL processes.
  • Experience analysing data across performance marketing channels
  • Experience analysing user journey behaviour and analysing drop offs, providing enhancements and recommendations.
    Desirable skills
  • Basic knowledge of CRO tools and creating tests
  • Exposure of CRM and email marketing analytics e.g Klaviyo
  • Basic knowledge of Performance Marketing platforms, Google Ads, Meta, TikTok to review data and pull through to reporting tools.
    Agency benefits:
  • Hybrid working (3 office days).
  • 25 days holidays + bank holidays.
  • Free onsite gym.
  • Birthday off.
  • Private health insurance.

    If you're interested in this role please click 'apply' or get in touch with Liv Grant @ KRG

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.