Data Analyst Tax, Global Tax Services

Amazon
London
1 week ago
Applications closed

Related Jobs

View all jobs

Static Data Analyst

Quants Analyst (AVP Level)

Business Analyst , Customer Experience and Business Trends (CXBT) Capability Team

Data Engineer

Lead BI Engineer

Market Facing Director - Databricks - AI & Data

Job ID: 2866299 | Amazon Spain Services, S.L.U.

The Data Analyst improves VAT Compliance reporting and data accuracy for Global Tax Services to reflect legislative and business change. In this role, you will work closely with indirect tax compliance customers and internal tech partners to support complex data solutions underpinning Amazon's indirect tax reporting in EMEA. As a Data Analyst, you will be expected to have solid data management experience, strong data querying skills, and understanding of tax and accounting principles to support indirect tax compliance teams in EMEA.

Key job responsibilities:

  1. Investigating and resolving data quality issues
  2. Defining technical requirements based on data analysis
  3. Performing user testing on new system builds
  4. Using Excel, SQL, Alteryx for reporting, auditing, and analysis
  5. Building relationships with key tech partners and business teams
  6. Identifying process gaps and improvements
  7. Escalating technical/operational issues appropriately
  8. Clearly explaining financial concepts and analysis to stakeholders
  9. Translating business objectives into actionable analyses
  10. Leading medium to large projects end-to-end, including establishing timelines, priorities, resource allocation, and keeping stakeholders informed of status and obstacles


About the team

The Global Tax Services (GTS) team aims to provide scalable, innovative indirect tax data solutions that increase efficiency and reliability. By implementing robust data management standards, we seek to reduce manual work while ensuring stability. Our focus is on delivering exceptional customer service and long-term value through solutions that enable operational scale.

BASIC QUALIFICATIONS

- Experience in tax, finance, or a related analytical field
- Experience in Excel (including VBA, pivot tables, array functions, power pivots, etc.) and data visualization tools such as Tableau
- Experience in business or financial analysis
- Experience defining requirements and using data and metrics to draw business insights
- Experience with SQL
- Experience making business recommendations and influencing stakeholders

PREFERRED QUALIFICATIONS

- Excellent communication skills; detail-oriented
- 3+ years of experience in data analytics, business intelligence, tax, or finance
- Bachelor’s degree

#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.

Careers

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.

Careers

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.

Careers

Data Science Leadership for Managers: Strategies to Motivate, Mentor, and Set Realistic Goals in Data-Driven Teams

Data science has become a linchpin in modern business, transforming oceans of raw data into actionable insights that guide strategy, product development, and personalised customer experiences. With this surge in data-centric operations, the need for effective data science leadership has never been more critical. Guiding a team of data scientists, analysts, and machine learning engineers requires not only technical acumen but also the ability to foster collaboration, champion ethical practices, and align complex modelling efforts with overarching business goals. This article provides practical guidance for managers and aspiring leaders aiming to excel in data-driven environments. By exploring strategies to motivate data science professionals, develop mentoring frameworks, and set achievable milestones, you will be better prepared to steer your team towards meaningful, evidence-based outcomes.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.