Analytics Engineer, Data Management

About the role

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

We are seeking an Analytics Engineer to join our internal Data Management team. As an Analytics Engineer, you will be part of the team transforming and delivering data for our Data Science, Analytics, and Reporting team use. You will be responsible for designing, implementing, and maintaining our data marts, pipelines, and data processes. This role requires a blend of strong technical skills, analytical thinking, and an ability to understand and communicate technical solutions with business partners.

Work model: full-remote from Romania

What You’ll Do

- Design, implement, and maintain scalable data marts and ETL/ELT processes using SQL or Python

- Transforming raw data from multiple sources into structured datasets for analysis and reporting

- Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs and turn those requirements into well-defined data models

- Build and orchestrate pipelines using Azure Data Factory and Databricks (Python notebooks and Spark SQL)

- Deliver and maintain recurring reporting datasets, including but not limited to Finance and Sales, such as quarterly business reviews and sales compensation

- Implement data quality checks and reconciliations so reported numbers can be trusted and traced back to their source

- Own operational health of the AE team’s pipelines: job scheduling and dependencies, monitoring, alerting on late or stale data, and root-cause analysis when something breaks

- Work closely with our internal Data, Architecture, and DevOps teams on deployments, infrastructure, and platform standards

- Document data flows, models, and processes clearly enough that a teammate can support them without you

What You’ll Bring

- 3+ years of experience in analytics engineering, data engineering, or BI engineering

- Advanced SQL, including window functions, CTEs, complex joins, and query performance tuning

- Python for data pipeline work (PySpark or pandas)

- Solid understanding of dimensional modeling and slowly changing dimensions (SCD Type 2)

- Experience building data processes on a cloud platform (Azure preferred; AWS or GCP considered)

- Experience running pipelines in production: scheduling, monitoring, and troubleshooting failures, not just building them