Hybrid Data Product Engineer for Analytics & AI

Metso; Lappeenranta; Hybrid; EUR 70,000 - 110,000; Full time

Benefits offered by this job

Hybrid working options

Competitive compensation

Wellbeing programs

Global collaboration

Job summary

Metso seeks a Data Product Engineer to design, build, deploy and operate scalable data products, pipelines and platform capabilities that enable analytics, digital solutions and AI across Metso.

You will transform data from multiple enterprise source systems into trusted, reusable and business-ready data assets, contributing throughout the data product lifecycle from design and implementation to deployment, monitoring and continuous improvement.

Qualifications

• Experience in data engineering, data integration and data products development.
• Proficient in SQL and Python with cloud data platforms experience.
• Knowledge of data modelling, transformation design and integration patterns.
• Understanding of CI/CD, DevOps or DataOps practices and secure software development.

Responsibilities

• Design, develop and maintain reusable data products and end-to-end pipelines.
• Contribute to enterprise data platforms with reusable patterns and automation.
• Improve scalability, performance and reliability of data systems.
• Embed data quality, testing, monitoring and observability in pipelines.
• Monitor data availability and investigate issues to implement fixes.
• Apply modern software engineering practices including CI/CD and DataOps.
• Ensure governance for metadata, lineage, security and compliance.
• Collaborate with architects, analytics teams and stakeholders throughout delivery.

Skills

SQL

Python

Data Engineering

Data Integration

Data Products

CI/CD

DevOps

Data Quality

Tools

Microsoft Fabric

Snowflake

Palantir

Databricks

Job description

Metso seeks a Data Product Engineer to design, build, deploy and operate scalable data products, pipelines and platform capabilities that enable analytics, digital solutions and AI across Metso.

You will transform data from multiple enterprise source systems into trusted, reusable and business-ready data assets, contributing throughout the data product lifecycle from design and implementation to deployment, monitoring and continuous improvement.