About the job
Veeam is recognized as the Data and AI Trust Company, dedicated to empowering organizations to gain a comprehensive understanding of their data and AI, ensuring they are secure and resilient to facilitate the safe scaling of AI technologies. As a frontrunner in data resilience and security posture management, Veeam is expertly positioned at the intersection of identity, data, security, and AI risk management. With its headquarters in Seattle and a presence in over 30 countries, Veeam safeguards the operations of more than 550,000 customers globally, who rely on us to keep their businesses thriving. Join us in our fearless journey forward, as we grow, learn, and make a significant impact for some of the most prominent brands in the world.
About the Role
We are seeking a talented Analytics Engineer to become a vital part of our internal Data Management team, enhancing data-driven decision-making throughout the organization. In this pivotal role, you will be responsible for designing and maintaining scalable data models, pipelines, and transformation processes that are essential for analytics, reporting, and data science initiatives. You will work in close collaboration with analysts, data scientists, and engineering teams to ensure the data is reliable, efficient, and structured to support valuable business insights and strategic objectives.
What You’ll Do
- Design scalable and dependable data marts and transformation processes to meet analytics and reporting requirements.
- Collaborate with data analysts, data scientists, and business stakeholders to comprehend data requirements and convert them into effective data solutions.
- Develop optimized data models and schemas that facilitate efficient storage, retrieval, and analysis of large datasets.
- Create ETL and ELT pipelines that transform raw data from diverse sources into structured datasets for analytics and reporting.
- Partner with data, architecture, and DevOps teams to ensure the scalability, performance, and reliability of data systems.
- Monitor data pipelines and systems to troubleshoot issues and identify areas for enhancement.
- Document data architectures, flows, and processes to promote transparency, collaboration, and maintainability.

