Consultant or Senior Consultant, Snowflake Engineer

Overview

As a Snowflake Engineer at Deloitte, you design and deliver scalable, AI-ready data solutions on the Snowflake Data Cloud to support analytics and ML initiatives. You will collaborate with clients and cross-functional teams to translate business needs into reliable data pipelines and data models. Your work strengthens data foundations, optimising performance, security, and governance in complex environments. This role offers hands-on cloud data engineering at scale with a focus on impact and continuous learning.

Pay / Benefits

• hybrid working policy
• London base
• growth and development opportunities
• focus on wellbeing
• collaborative culture
• impactful client work

Responsibilities

• Engage with technical stakeholders and delivery leads to gather requirements and deliver pipelines aligned with architecture
• Design and implement physical data models covering acquisition, preprocessing, ETL/ELT and deployment
• Support legacy data migrations for on-prem systems with emphasis on scale, efficiency and reliability
• Assess Snowflake technologies and integrate relevant changes into client solutions
• Contribute to knowledge sharing, reusable code and CI/CD practices across sprint teams
• Design, build, test and deploy scalable batch and near-real-time data pipelines and analytics solutions on Snowflake using SQL, Python, Snowpark, Snowpipe, Streams, Tasks and Dynamic Tables
• Manage full data engineering lifecycle from requirements analysis through deployment, operability and improvement
• Communicate solution options, trade-offs and dependencies clearly to technical and non-technical audiences
• Develop robust data models and ETL/ELT patterns supporting analytics, ML and AI use cases
• Optimise pipelines and Snowflake workloads for performance, reliability, scalability and cost with mitigations for engineering risks
• Embed security, RBAC, governance, lineage and data quality using Snowflake features (masking, secure data sharing, account usage views)
• Prepare trusted, governed datasets and integrate data products with downstream applications for analytics and AI
• Apply modern engineering practices including version control, automated testing, CI/CD, IaC, monitoring and data observability
• Produce clear technical artefacts and deployment documentation; participate in technical reviews and establish standards

Key requirements

• Bachelor’s degree in a technical or quantitative field; equivalent acceptable
• SnowPro Core certification (mandatory)
• Experience delivering data solutions in production environments and regulated/data-rich industries
• Experience gathering requirements from business and technical stakeholders and communicating delivery progress
• Strong analytical, problem-solving and troubleshooting abilities
• Experience developing or supporting cloud data warehouses on Snowflake with AWS/Azure/GCP
• Proficiency in SQL, Python, Snowpark, Snowpipe, Streams, Tasks, Dynamic Tables and data stack frameworks
• Knowledge of Snowflake architecture, including stages, file formats, virtual warehouses, secure data sharing, RBAC, Time Travel, cloning and replication
• Experience with query optimisation, warehouse configuration, resource monitors and account usage views
• Experience with CI/CD, IaC, automated testing and coding guardrails; ability to explain technical concepts to diverse audiences
• Ability to build solutions using Snowflake Cortex Code
• Strong understanding of data modelling for analytics, reporting and AI use cases
• Knowledge of cloud-native security, governance and access management; experience enabling AI-ready workloads
• Exposure to DevOps-style tools and Terraform for infrastructure as code
• Experience with orchestration technologies and cloud integration services
• Ability to explain technical concepts clearly and adapt messaging to different audiences

• Collaborative mindset
• Clear communication with both technical and business stakeholders
• Problem-solving orientation and pragmatic approach
• Snowflake architecture and features (Time Travel, cloning, data sharing)
• Snowpark, Snowpipe, Streams, Tasks, Dynamic Tables
• SQL and Python for data engineering

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