Data Architect - job 1 of 2

As a Data Architect at e.l.f. Beauty, Inc., you're responsible for developing and maintaining the overall data architecture strategy and roadmap. You'll design data models, oversee data integration processes, manage data security, and ensure compliance with governance policies. Your role also includes optimizing data flows, leading data migration projects, and collaborating with vendors to enhance our data infrastructure.

Candidates for the Data Architect role at e.l.f. Beauty, Inc. should hold a Bachelor's or Master's degree in Computer Science or a related field, along with 18+ years of IT experience and 8+ years specifically in data architecture. Knowledge of data warehousing, ETL/ELT processes, strong SQL skills, and experience with cloud platforms are essential for success in this position.

At e.l.f. Beauty, Inc., the work environment for a Data Architect is dynamic and collaborative. You’ll enjoy a hybrid work model that combines three days in the office and two days at home, along with flexible time off and a culture that emphasizes innovation and teamwork. This supportive atmosphere empowers you to foster creativity and maintain high standards in data management.

A Data Architect at e.l.f. Beauty, Inc. is expected to be familiar with various tools and technologies, including SQL for database management, cloud computing platforms like AWS and Azure, and programming languages such as Python or Java. Additionally, knowledge of data security practices and emerging technologies is crucial for optimizing our data architecture.

e.l.f. Beauty, Inc. is committed to the continuous development of its team members, including Data Architects. We encourage participation in workshops, conferences, and ongoing training to stay ahead of industry trends. Our culture of mentorship allows you to grow your skills while collaborating with experienced colleagues across the organization.

When discussing your experience with data modeling, it’s beneficial to provide specific examples from past projects where you designed data models to meet business needs. Highlight the methodologies you used, such as dimensional modeling or normalized structures, and explain how your design choices improved data integrity and accessibility.

In crafting your response, focus on specific strategies you've employed to establish data quality standards and governance policies. Discuss how you implemented monitoring systems for data quality, trained team members on governance procedures, and used tools to track compliance, emphasizing the positive impact these had on data reliability.

When addressing this question, share your approach to continuous learning, mentioning resources like industry publications, online courses, and attending relevant conferences. Highlight any specific technologies you recently explored and how you applied that knowledge to enhance your projects.

To effectively answer this, narrate the specific challenges you encountered during a data integration project, such as dealing with disparate data sources or tight deadlines. Explain the steps you took to overcome these challenges, the tools you used, and how the successful completion of the project benefited the organization.

Here, discuss techniques you use to optimize data pipelines, such as implementing efficient ETL processes, reducing data redundancy, or leveraging data storage solutions for performance. Providing tangible results from previous optimization efforts will showcase your expertise and its impact on project efficiency.

Your response should emphasize your communication skills and ability to simplify complex concepts. Discuss using visual aids or analogies and tailoring your explanations to the audience's level of understanding, demonstrating your commitment to ensuring everyone can grasp the information.

In your answer, highlight specific benchmarking techniques you've employed, such as performance testing under different loads or profiling slow queries. Discuss how these techniques helped identify bottlenecks and improve system performance, showcasing your analytical skills.

When answering this question, provide a detailed account of a successful collaboration with teams such as data scientists or business analysts. Describe your role, the communication techniques you used, and the project outcomes, emphasizing how collaboration led to innovative solutions.

Discuss your hands-on experience with cloud-based platforms, such as AWS or Azure, and the services you've utilized for data storage and processing. Highlight specific projects where you migrated data to the cloud and the benefits achieved, such as improved scalability and cost-efficiency.

Relate your methods for prioritizing tasks, such as assessing project impact, deadlines, and resource availability. Share examples of how you've successfully managed competing priorities, ensuring that all projects met their objectives while maintaining quality.