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.

Work arrangement
Hybrid