Senior Business Analyst, GTS- Audit

Amazon
London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Senior Business Analyst - Insurance

Senior Business Analyst - Insurance Retail

Senior Business Analyst

Senior Business Analyst - Fixed Income

Senior Business Analyst, Global Tax Services

Business Analyst - Guest Data & CRM Systems

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

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.