Data Engineer

Insight Global are seeking a Snowflake Data Engineer for a large enterprise organisation undergoing a significant cloud and data transformation programme.

The successful candidate will be responsible for designing, building, and optimising modern data pipelines and cloud-based analytical environments. They will work closely with stakeholders across the business to support enterprise data initiatives, enhance platform performance, and enable advanced analytics capabilities, including AI/ML integrations.

Must-Have Requirements Include:

• Strong expertise in Snowflake data engineering and platform administration.
• Advanced SQL development experience, including ANSI SQL, stored procedures, and user-defined functions.
• Hands-on experience building and managing Snowflake Virtual Warehouses, including sizing, scaling, and multi-cluster configurations.
• Experience optimising Snowflake performance through query tuning, clustering keys, caching strategies, and warehouse configuration.
• Knowledge of Snowflake Streams and Tasks for real-time ingestion, CDC processes, and workflow automation.
• Experience leveraging Snowflake Time Travel and Fail-Safe features for data recovery and auditing.
• Strong background building and maintaining ETL/ELT pipelines.
• Experience with Snowpipe for data ingestion and pipeline orchestration.
• Proficiency in Python for automation, scripting, and data engineering workflows.
• Hands-on experience with Snowpark or in-database processing frameworks.
• Experience integrating Snowflake with Azure cloud services.
• Strong understanding of data warehousing concepts and dimensional data modelling.
• Experience implementing data governance, security controls, RBAC, encryption, and secure data-sharing solutions.
• Familiarity with CI/CD pipelines and DevOps/DataOps practices supporting data engineering environments.Plusses Include:
• Experience integrating Snowflake with AWS services and multi-cloud architectures.
• Exposure to Snowflake Cortex and AI/ML-driven analytics solutions.
• Knowledge of large language model (LLM) workflows and generative AI technologies.
• Experience developing automated deployment, testing, and environment management processes.
• Familiarity with enterprise-scale analytics, reporting, and business intelligence platforms.
• Experience supporting modern data platforms within highly regulated or large-scale enterprise environments.