Senior Business Analyst, GTS- Audit

Amazon
London
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Business Data Analyst

Senior Business Data Analyst

Hybrid Senior Regulatory Data Analyst in Gambling

Senior Data Analytics & AI Lead - Remote

Senior Business Data Analyst: Power BI & Data Mastery

Senior Business Data Analyst — Power BI & ERP Analytics

Job ID: 2795867 | Amazon Spain Services, S.L.U.

Amazon is seeking a highly motivated Senior Data Analyst to join GTS- Audit team. In this role, you will be driving audit data support requests by understanding the requirements, planning, scoping, executing, and providing data solutions to our business customers. This team sits within Global Tax Services and is seeking an exceptionally capable individual to help deliver Tax Technology support within the Audit team for our Indirect Tax function. This position is based in Barcelona or Bratislava.
Ideally, we are looking for candidates with strong data analytical skills along with Tax experience. This role requires a self-starter with a keen attention to detail and a good track record of meeting deadlines. The successful candidate will have the ability to tackle multiple requests and efficiently execute deliverables. You will use your analytical skills to interpret clearly, analyze quantitatively, problem-solve, scope technical requirements, and prioritize.
Come innovate with the Amazon Global Tax Services Team!

Key job responsibilities

As Senior Business Analyst, you are expected to support Indirect Tax Audits globally and work in support of Audit Readiness. Your responsibilities include:

  1. Supporting the indirect Tax team on Tax audits on a daily basis.
  2. Diving deep into the details to develop meaningful findings and provide required data.
  3. Analyzing and solving problems at their root, understanding the broader context.
  4. Owning end-to-end ‘Audit request’ cases from gathering requirements to solutions, ensuring deliverables within the deadline.
  5. Learning and understanding a broad range of Amazon’s data resources and knowing when, how, and which to use.
  6. Documenting processes, data flows, etc.
  7. Building partnerships with Tax, Finance, and Accounting customers.

BASIC QUALIFICATIONS

• BS degree in Accounting, Business, Data Science, Economics, Finance, Mathematics, or a related field or equivalent experience.
• Substantial experience as a business analyst, data analyst, statistical analysis, or data engineering role within a technology environment.
• Advanced proficiency in SQL, Excel, and any data visualization tools like Tableau or similar BI tools.
• Advanced ability to draw insights from data and clearly communicate them to stakeholders and senior management.
• Proficiency with Alteryx.
• Strong analytical skills – ability to start from ambiguous problem statements, identify and access relevant data, make appropriate assumptions, perform insightful analysis, and draw conclusions relevant to the business problem.
• Demonstrated ability to communicate complex technical problems in simple terms.
• Excellent writing skills – experience in writing business documents, process flows, and building flowcharts.
• Ability to present information professionally and concisely with supporting data.

PREFERRED QUALIFICATIONS

• Experience within Tax/Accounting/Finance.
• Familiarity with APIs, JavaScript, and Python.
• Knowledge of data management and modeling fundamentals and data storage principles.
• Experience with Amazon tools, for example, AWS.

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify, and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use, and transfer the personal data of our candidates.

Posted:January 31, 2025 (Updated 3 days ago)

Posted:November 22, 2024 (Updated 6 days ago)

Posted:January 22, 2025 (Updated 15 days ago)

Posted:January 22, 2025 (Updated 15 days ago)

Posted:January 20, 2025 (Updated 17 days ago)

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

#J-18808-Ljbffr

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.

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

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.