Senior Business Analyst, GTS- Audit

Amazon
London
2 months ago
Applications closed

Related Jobs

View all jobs

Technical Business Analyst - Senior Consultant

Business Analyst - Senior Consultant

Senior Data Analyst

Senior Analyst & Data Specialist

Business Data Analyst

Quantitative Business Analyst

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.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.