IT & Software

Consultant or Senior Consultant, Snowflake Engineer

Deloitte

London · Greater London · United Kingdom

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

Reference: WJ-747_31054142

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