Senior Data Engineer (Snowflake)

LinkGroup is recruiting for a Senior Data Engineer to join a growing team in Kraków working on a mission-critical, AI-powered SaaS product supporting high-value business decisions.

• Strong hands-on experience with Snowflake including data modelling, performance tuning, cost optimisation, and security/governance.
• Proven experience building production-grade ELT/ETL pipelines using tools such as dbt, Apache Airflow, or Azure Data Factory.
• Expert-level SQL skills.
• Working knowledge of at least one general-purpose programming language, preferably Python, for automation and data processing.
• Practical experience using AI-assisted development tools like Claude or Cursor to improve development speed, quality, and documentation.
• Strong data quality mindset with experience in testing, monitoring, alerting, and observability.
• Ability to take ownership of initiatives from unclear requirements to reliable data products.
• Strong communication skills to explain technical data topics to business stakeholders.

Nice to have:

• Experience with Power BI including semantic models, DAX, and Power Query.
• Experience in finance, accounting, fintech, or regulated environments.
• Familiarity with Microsoft Fabric and data mirroring from Azure sources.
• Experience with cloud-native data architectures in Azure or AWS such as Azure Synapse, Azure Data Lake, AWS Glue, or S3.
• Experience with streaming or event-driven ingestion using Kafka or Azure Event Hubs.
• Understanding of Responsible AI principles and data infrastructure for auditable AI/ML pipelines.
• Interest in modern data stack trends, open-source tools, or emerging analytics engineering practices.

• Build and maintain reliable ELT/ETL pipelines from transactional systems, microservices, event streams, and external sources into Snowflake.
• Design clean, performant, and scalable data models for reporting, analytics, and customer insights.
• Contribute to the architecture of the end-to-end data platform covering ingestion, transformation, serving, and analytics.
• Apply modular, tested, and well-documented data modelling practices.
• Use AI development tools such as Claude and Cursor for pipeline development, query optimisation, documentation, and quality improvements.
• Implement data quality checks, automated testing, monitoring, and observability in data pipelines.
• Support code reviews, documentation, and knowledge sharing regarding Snowflake, data modelling, performance optimisation, and best practices.
• Collaborate closely with Software Engineers, Product Managers, Finance stakeholders, and analytics teams to translate data needs into data products.