Tietoevry
Computer Software
DataEngineer
Neural analysis suggests this role is
optimal for mid candidates.
“Data Engineer at Tietoevry. Skills: Data Engineering, Analytics Engineering, Power BI, Microsoft Azure, data modeling, ETL/ELT. Design and evolve scalable data pipelines. build semantic and analytical models for Power BI”
What You'll Achieve.
enables analytics and AI across our digital ecosystem; directly impact decision-making and AI capabilities
Industry & Context.
turn complex and fragmented data into trusted, well-structured information; Investigate production data to identify noise, inefficiencies, and optimization opportunities
What They're Looking For.
Must Have
Data Engineering, Analytics Engineering, Power BI, dimensional/data modeling, ETL/ELT architectures, modern data pipelines, Microsoft Azure data services, cloud-based data platforms, SQL skills, Python, production-grade datasets, operational environments, Microsoft Fabric
Nice to Have
Azure certifications, Power BI certifications, data quality, data lineage, data observability concepts, CI/CD practices applied to data workflows, AI/ML projects from a data provisioning perspective
What You'll Do.
Design and evolve scalable data pipelines
build semantic and analytical models for Power BI
ensure data is consistent
and well-governed across Microsoft Azure and Fabric environments
work closely with AI and ML teams to deliver high-quality datasets
support feature engineering
improve how production data is collected
Design and evolve robust ETL/ELT pipelines for analytics and AI workloads
Build and maintain high-quality Power BI models and curated datasets
Define and enforce data quality
and consistency standards
Collaborate with AI/ML teams to deliver structured
feature-ready datasets
Investigate production data to identify noise
and optimization opportunities
Contribute to data observability practices and help define meaningful data signals
Maintain and improve data workflows within Azure and Microsoft Fabric ecosystems
How You'll Work.
Team & Collaboration
Work cross-functionally with Data, AI, DevOps, Testing, and Platform teams; Collaborate with AI/ML teams to deliver structured, feature-ready datasets
Full Job Description
We are looking for aData Engineer to turn complex and fragmented data into trusted, well-structured information that enables analytics and AI across our digital ecosystem. In this role, you will design and evolve scalable data pipelines, build semantic and analytical models for Power BI, and ensure data is consistent, reliable, and well-governed across Microsoft Azure and Fabric environments. You will work closely with AI and ML teams to deliver high-quality datasets, support feature engineering, and improve how production data is collected, interpreted, and used. This position is ideal for someone who enjoys shaping the foundation of data-driven products—transforming raw information into meaningful insights that directly impact decision-making and AI capabilities. Responsibilities * Design and evolve robust ETL/ELT pipelines for analytics and AI workloads * Build and maintain high-quality Power BI models and curated datasets * Define and enforce data quality, governance, and consistency standards * Collaborate with AI/ML teams to deliver structured, feature-ready datasets * Investigate production data to identify noise, inefficiencies, and optimization opportunities * Contribute to data observability practices and help define meaningful data signals * Work cross-functionally with Data, AI, DevOps, Testing, and Platform teams * Maintain and improve data workflows within Azure and Microsoft Fabric ecosystems Qualifications * 3–5 years of experience in Data Engineering, Analytics Engineering, or similar roles * Strong hands-on experience with Power BI and dimensional/data modeling * Solid understanding of ETL/ELT architectures and modern data pipelines * Experience with Microsoft Azure data services and cloud-based data platforms * Strong SQL skills and proficiency in Python or similar scripting languages * Experience working with production-grade datasets and operational environments * Hands-on experience with Microsoft Fabric * Exposure to AI/ML projects from a data
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