Senior Data Engineer, Data Platforms

We are seeking an experienced and results-driven professional to join Valmet s Data Platforms Team as a Data Engineer (2 position). In this role, you will play a key part in delivering and executing data-driven development and services that support global data and analytics initiatives.

The ideal candidate brings 5-10 years of proven expertise in data platforms, data modelling, and data architecture principles, including data lakes, data warehouses, metadata management, and governance. You should have hands-on experience in designing, building, and maintaining scalable, reliable data pipelines for collecting, transforming, and loading (ETL) data from diverse sources into storage systems. Additionally, you will be expected to optimize pipelines for performance, scalability, and cost efficiency.

Areas of responsibility

• Execution of data and analytics initiatives development and services.

• Align deliveries with architectural guidelines and ensure quality in delivery projects.

• Support external AMS team in major incidents and recurring issues.

• Support root cause analysis and implementation of long-term fixes.

Basic requirements of the position

• Bachelor s or higher degree in Computer Science, Engineering, or related field.

• Solid understanding of data and analytics platforms, data modelling, and data architecture principles (data lake, data warehouse, metadata, governance).

• Proven experience in Microsoft Fabric or similar data and analytics platforms.

• Experience in Microsoft Azure services, including data platform related technologies.

• Knowledge of data integration tools and techniques for ingesting and processing data from diverse sources.

• Excellent communication and interpersonal skills to facilitate effective collaboration with diverse teams.

Competencies and skills of the position

• Fluent spoken and written English language

• Familiarity with Western business practices and work culture will be added advantage.

• Translate business requirements into data models

• Define data products and SLAs

• Metadata-driven discovery and self-service analytics

• Collaboration with data scientists, analysts, and governance teams

• Technical, business, and operational metadata

• Data lineage (column-level, table-level)

• Data classification & tagging