Senior Data Engineer (Snowflake)

Link Group is hiring for a Senior Data Engineer role focused on Snowflake data platform, working on production-grade ELT/ETL pipelines, data modelling, and analytics solutions.

• Strong hands-on experience with Snowflake, including data modelling, performance tuning, cost optimization, and security/governance.
• Proven experience building production-grade ELT/ETL pipelines using dbt, Apache Airflow, Azure Data Factory, or similar tools.
• 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 such as Claude or Cursor.
• Strong data quality mindset, including testing, monitoring, alerting, and observability.
• Ability to take ownership of initiatives from unclear requirements to reliable data products.
• Strong communication skills and ability to explain technical topics to business stakeholders.

Nice to have:

• Experience with Power BI, semantic models, DAX, Power Query, report design, row-level security, incremental refresh, and Power BI Service.
• Experience in finance, accounting, fintech, or regulated environments.
• Familiarity with Microsoft Fabric and Azure-hosted data mirroring.
• Experience with cloud-native data architectures like Azure Synapse, Azure Data Lake, AWS Glue, or S3 data lake patterns.
• Experience with streaming/event-driven ingestion using Kafka, Azure Event Hubs, or similar.
• Understanding of Responsible AI principles and AI/ML pipeline data infrastructures.
• Interest in modern data stack trends, open-source tooling, or emerging analytics engineering practices.

• Build and maintain reliable ELT/ETL pipelines moving data into Snowflake from various sources.
• Design clean, performant, and scalable data models for reporting and analytics.
• Contribute to the architecture of the end-to-end data platform—from ingestion to analytics.
• Apply modern, modular, tested, and well-documented data modelling practices.
• Use AI development tools such as Claude and Cursor to accelerate development, query optimization, documentation, and quality improvements.
• Implement data quality checks, automated testing, monitoring, and observability in data pipelines.
• Support code reviews, documentation, and knowledge sharing around Snowflake, data modelling, and performance optimization.
• Collaborate closely with Software Engineers, Product Managers, Finance stakeholders, and analytics teams to translate data needs into data products.