AI

Business Intel Engineer III, Strategic Initiatives – Amazon

Amazon
Dubai Full-time

Applications close Dec 5, 2026

Job Description

Amazon is hiring a Business Intelligence Engineer III for its Strategic Initiatives team in Dubai. The posting describes a role that will play a critical part in shaping and driving the data analytics stream within the team, and it references experience with statistics and machine learning tools, including work in areas such as recommendations.

Business Intelligence Engineers at Amazon sit between data engineering and analytics. At the III level, you would typically own end-to-end analytical solutions: designing data models and pipelines, writing complex SQL, building dashboards and reporting in tools such as QuickSight, and carrying out deep-dive analyses that answer ambiguous business questions. In a strategic initiatives team, those questions often relate to new programmes, market expansions or major changes, where there is no established playbook and the data foundations may need to be built from scratch.

The machine learning element suggests the role goes beyond descriptive reporting. You may work on models or statistical approaches to understand customer behaviour, forecast outcomes, measure the impact of initiatives through experiments or causal methods, or support recommendation and personalisation use cases, often in partnership with data scientists and engineers.

Amazon’s culture is strongly data-driven and relies on written narratives, so you will be expected to define metrics, explain what the data shows in clear documents and influence decisions with evidence. The role suits an experienced BI engineer or analytics professional with strong SQL and data modelling skills, practical statistics and ML experience, and the independence to drive an analytics workstream in a fast-moving environment.

Impact of the Job

Strategic initiatives are, by nature, high-stakes bets, and good data is what allows leaders to make them wisely. A Business Intelligence Engineer III who builds solid data foundations and clear metrics gives the team visibility into how new programmes are performing and where to adjust. Without that, initiatives can run for months before problems are noticed. Deep-dive analyses help leaders understand cause and effect rather than just trends, and statistical and machine learning methods can measure the true impact of initiatives, forecast outcomes and uncover opportunities that simple reporting would miss. Recommendation and personalisation work can directly improve customer experience and conversion. Well-built, reusable data pipelines and dashboards also save time across teams and reduce errors from manual reporting. By owning the analytics workstream, the engineer ensures consistency and quality across the team's decisions, enabling Amazon to move quickly while staying grounded in evidence. That is the core value of the role.

Job Seeker Benefit

A Business Intelligence Engineer III role at Amazon offers the chance to shape the analytics behind strategic initiatives at one of the world's most data-driven companies. You will combine data engineering, analytics and machine learning, building a versatile skill set that is highly valued across technology, e-commerce and consulting. Working in a strategic initiatives team exposes you to new and ambiguous problems, which accelerates learning and develops your ability to create structure where none exists. Amazon's culture of ownership and written communication strengthens how you think and present your work, helping you influence senior leaders effectively. You will gain experience with AWS data tools and large-scale data, keeping your technical skills current. Over time, this role can lead to senior BIE, analytics manager, data science or analytics leadership positions, within Amazon or elsewhere. The machine learning exposure also offers a pathway toward more specialised data science work if that interests you. Working in Dubai places you within Amazon's growing regional operations, offering visibility and the chance to contribute to businesses serving fast-growing markets. For an experienced analytics professional who wants to own a significant workstream and see their work drive important decisions, this role offers strong career growth. It is a role with real scope.

Suggested Resume Keywords

Consider including these terms in your resume for this role:

business intelligence engineerBIEdata analyticsSQLdata modellingETLQuickSightTableauPythonstatisticsmachine learningrecommendation systemsAWS Redshiftmetrics definitionexperimentation

Requirements

  • Bachelor’s degree in computer science, engineering, statistics, mathematics or a related quantitative field; a master’s degree is an advantage
  • Significant experience in business intelligence, analytics engineering or data analysis
  • Advanced SQL skills and experience with data modelling and ETL pipelines
  • Experience with BI and visualisation tools such as QuickSight, Tableau or Power BI
  • Proficiency in Python or R for statistical analysis
  • Practical experience with statistics and machine learning tools; exposure to recommendation systems is an advantage
  • Experience with AWS data services such as Redshift, S3 or Glue is a plus
  • Ability to define metrics and translate ambiguous questions into analytical solutions
  • Strong written communication and stakeholder management skills

Interview Questions to Prepare For

How would you set up metrics and data infrastructure for a brand-new strategic initiative?

Defining goals, input vs output metrics, data sources, scalable pipelines

Tell me about an analysis that changed a senior leader's decision.

Influence, rigour, clarity

How would you measure the true impact of an initiative when you can't run a clean A/B test?

Causal methods, difference-in-differences, matching, caveats

Describe a data pipeline you built and how you ensured data quality.

Engineering practice, validation, monitoring

How have you used machine learning in your analytics work?

Practical application, appropriate methods

Walk me through how you would approach a recommendation problem.

Problem framing, data, evaluation metrics

How do you write a narrative that explains complex findings to non-technical leaders?

Structure, clarity, focus on implications

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