Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Reporting Analyst

XP Power
Pangbourne
9 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst (Reporting & Operations)

Data Analyst (Reporting & Operations)

HR Reporting Data Analyst

HR Reporting Data Analyst

HR Reporting Data Analyst

HR Reporting Data Analyst

Job Description

We are seeking a skilled and detail-oriented Reporting Analyst to join our team in Pangbourne, United Kingdom. As a Reporting Analyst, you will play a crucial role in transforming complex data into actionable insights that drive business decisions. This position offers an exciting opportunity to work with cutting-edge technologies and collaborate with cross-functional teams to deliver high-quality reports and analytics solutions.

  • Managing ad-hoc data extraction and transformation requests to support business needs for data visibility. 

  • Support a new initiative for business self-serve analytics with the provision of common comprehensive and re-usable datasets. 

  • Provision of visuals in Power BI to meet business reporting requirements. 

  • Strong working knowledge of technologies including SQL, Azure and Databricks to compile underlying data models to support reporting. 

  • Support the ingestion process and scheduling of source raw data tables into the XP data warehouse. 

  • Liaison with the business and SAP Data Architect to understand data requirements for extraction in preparation for reporting. 

  • Support the business with any general reporting issues, queries and management of enhancement requests. 

  • Maintenance and creation of key documentation to support BI and the XP reporting catalogue. 


Qualifications

  • Bachelor's degree in Business, Computer Science, Statistics, or a related field
  • Proven experience working with Power BI and strong SQL skills for data querying and transformation
  • Proficiency in developing data cubes to support self-serve reporting, including requirements gathering, design, build, deployment, and training
  • Experience working with SAP systems, specifically in reporting and data analysis
  • Exposure to Microsoft Azure Data Factory and Databricks
  • Strong analytical mindset with the ability to interpret complex data sets and generate meaningful insights
  • Excellent documentation skills, including the ability to translate business requirements into technical specifications and create clear user guides
  • Detail-oriented with a strong focus on data accuracy and quality
  • Outstanding communication skills, with the ability to present complex problems in a simple manner to non-technical audiences
  • Experience with data visualization tools and knowledge of data warehousing concepts
  • Ability to engage with stakeholders at all levels and work collaboratively in a team environment
  • Self-motivated with excellent organizational skills and the ability to work independently
  • Proven track record of delivering efficient and innovative reporting solutions



Additional Information

Location

  • Based in the UK
  • Hybrid 2 days in the Pangbourne Office and 3 days from home

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.