Data Analytics Engineer

Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world’s biggest brands.
About the Role
Reporting directly to the Director, Data Analytics, the Data Analytics Engineer contributes to the technical design, development, and enhancement of Corporate Metrics data products at Veeam, including Annual Recurring Revenue (ARR). The role implements the technical roadmap set by the Director for these data assets to drive data-driven decision making across the organization. The ideal candidate brings strong data architecture and dimensional modeling expertise, clear communication, strategic thinking, and the ability to translate business needs into scalable, reliable, and well-governed data assets.
This role is integral to the transformation of Veeam’s Corporate Metrics, which support both internal and external reporting and analytics. Responsibilities include architecture and technical design, building dimensional data models, understanding business processes, and driving execution to deliver accurate and scalable solutions aligned to a roadmap. The Data Analytics Engineer implements team processes and collaborates with partners and business stakeholders to translate requirements.
What You’ll Do
Maintain Corporate Metrics Data Assets: Create and maintain the data pipelines, models, and storage solutions behind the Corporate Metrics data mart, including ARR, ensuring completeness, accuracy, and security.
Corporate Metrics Semantic Layer: Build out a robust semantic layer enabling business users to access corporate metrics through AI Tools in natural language.
Dimensional Data Modeling: Design and maintain dimensional models (star and snowflake schemas, conformed dimensions, fact tables, slowly changing dimensions) that serve as a consistent, reusable foundation for reporting and analytics across metric domains.
Data Architecture & Transformation Job Development: Design and implement robust, scalable, and efficient Databricks jobs to support sources of truth for Corporate Metrics reporting and analytics.
Data Quality & Governance: Collaborate and communicate requirements with the Data Quality and Governance team around data lineage, segmentation and sources of truth for Corporate Metrics data assets.
Collaboration: Work closely with the Central Data Engineering, Revenue Intelligence, and Corporate Technology teams to align on metric definitions, requirements, and reporting needs.
Communication: Act as the technical point of contact for Corporate Metrics data assets. Help translate business requirements into technical specs.
Continuous Improvement: Identify opportunities to improve data processes, automation, and reporting capabilities.
Release Management: Manage app changes through source control (e.g., GitHub or Azure DevOps), code review, and testing, following change processes that meet SOX and other compliance requirements.
Key Success Factors
Understanding of Business Strategy and Processes related to Corporate Metrics:

Skills
Data Analysis, Git, Azure, Snowflake, Databricks, Data Modeling