Reporting Analyst

XP Power
Pangbourne
11 months ago
Applications closed

Related Jobs

View all jobs

HR & Payroll Data Analyst for System Upgrade & Reporting

Data Analyst (Banking Domain)

Data Analyst

Data Analyst

Data Analyst

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.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

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.