Must have

• SQL

• ETL

• DWH

• Redshift

• Qlik

• English

Requirements description

• 5+ years of experience as a Data Engineer or ETL Developer, building large-scale data pipelines in a cloud environment (AWS, GCP, or Azure).
• Strong SQL expertise, including query optimization and data modeling.
• Hands-on experience with ETL/ELT tools such as Matillion, Rivery, SSIS, Talend, or similar.
• Solid understanding of data warehouse concepts and dimensional modeling.
• Excellent analytical and problem-solving skills.
• A collaborative mindset and the ability to work cross-functionally with internal teams.
• A self-starter and agile learner who thrives in a fast-paced, dynamic environment.
• AI/Data-related development capabilities — experience building or integrating AI-driven data solutions is a plus.
• Experience with Redshift and Matillion - big advantage.
• Experience with BI tools such as Qlik or Power BI - big advantage.
• Familiarity with CI/CD pipelines.

Offer description

Matrix Eastern Europe, the offshore division of Matrix IT, one of the leading global R&D services companies with more than 11, 000 professionals, is looking for a Senior Data Engineer to join the team of our client and partner – Stampli!

They’re looking for a Senior Data Engineer to join their Data team - someone who’s passionate about building reliable, scalable data infrastructure and thrives on solving complex technical challenges.

In this role, you’ll own the design and development of end-to-end data pipelines that power Stampli’s analytics and data-driven decision-making. You’ll collaborate closely with both business and technical stakeholders to ensure data flows smoothly, accurately, and efficiently across the company.

Your responsibilities

1. Design, implement, and maintain large-scale ETL and ELT pipelines using modern data frameworks and cloud technologies.
2. Work with Redshift data warehouses to design efficient schemas and optimize performance.
3. Build and manage data ingestion processes from multiple sources - APIs, SaaS platforms, internal systems, and databases.
4. Collaborate with stakeholders to deliver clean, well-modeled, and high-quality data.
5. Build and evolve a modern, efficient, and scalable data warehouse architecture.
6. Ensure observability, monitoring, and testing across all data processes.

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