Business Intelligence Engineer, Intelligent Talent Acquisition
Content + Source + Freshness • 12 Dec 2025 • 95% confidence
Offer value
This role offers significant opportunities for impact and innovation in talent acquisition, enabling transformative data capabilities.
- High impact role in recruitment at Amazon
- Innovation opportunities with advanced AI systems
- Potential for substantial personal and career growth
- Collaboration across diverse teams and departments
Pros
- Ability to shape hiring processes at scale.
- Work with advanced AI and machine learning technologies.
- Direct impact on improving the candidate experience.
Cons
- High expectations for immediate results can be daunting.
- Requires adaptability to fast-changing projects.
- Complexity of tasks may be overwhelming for some.
Who it's for
Mid-Level • Onsite/Remote Hybrid
Good fit
- Mid-level professionals in HR analytics
- Data-oriented individuals focused on recruitment
- Innovative thinkers eager to improve hiring processes
Not recommended for
- Candidates without a data analytics background
- Individuals looking for static, unchanging roles
- Those uncomfortable with technology advancements
Motivation fit
Key skills
About the job
DESCRIPTION
Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA), you'll have the opportunity to reinvent the hiring process and deliver unprecedented scale, sophistication, and accuracy for Amazon Talent Acquisition operations. ITA is an industry-leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise. Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time. You’ll work on state-of-the-art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in-house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives. Together, we can solve the world's toughest hiring problems.
The Business Intelligence Engineer role in ITA is responsible for creating data modeling necessary to facilitate reporting and analytics, acquiring new data sources, and building out comprehensive data visualizations for stakeholders. This role is truly a jack of all trades; it will be part process improvement, part analytics, part data modeling, and part strategic design. If you can seamlessly move across these various skillsets then you’ll thrive in this role.
Key job responsibilities
The role requires a uniquely gifted analytics expert who is naturally curious and runs towards big challenges. If you are passionate and curious about data, obsess over customers, love questioning the status quo, and want to see and feel a tangible impact to your team’s work, this is the role for you.
Key Responsibilities Include:
* Solving complex business problems with simple, analytical reports, dashboards, visualizations, and analyses.
* Partnering with our Data Engineering and Data Science teams to design, build, and measure data models and metrics using data driven tools.
* Building out comprehensive metrics to measure operational health, then demonstrate continuous improvement within these metrics.
* Being the voice of your customers; communicate the needs of candidates, associates, and staffing coordinators to your stakeholders via the data.
About the team
The Intelligent Talent Acquisition (ITA) team supports the Amazon Talent Acquisition (ATA) organization by transforming data into insights on hourly associate and corporate hiring within Amazon. While ATA is responsible for the management of the hourly and corporate talent supply chain, the ITA team in particular focuses on defining analytics that measure the performance of the program. While we continue to work on metrics that help monitor the health of the program we also spend efforts on identifying efficiencies in the process path through the power of data and predictive analytics.
