Consultant - Snowflake Data Engineer

Job Requirements

Mandatory Skills

• Bachelor’s degree in computer science, Data Science, engineering, mathematics, information systems, or a related technical discipline

• 5+ years of relevant experience in data engineering roles

• Detailed knowledge of data warehouse technical architectures, data modelling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding

• Proficient in at least one or more programming languages: Java, Python, Ruby, Scala

• Experienced with AWS services such as Redshift, S3, EC2, Lambda, Athena, EMR, AWS Glue, Datapipeline.

• Exposure to data visualization and reporting with tools such as Amazon QuickSight, Metabase, Tableau, or similar software

• Experience building metrics deck and dashboards for KPIs including the underlying data models.

• Understand how to design, implement, and maintain a platform providing secured access to large datasets

Primary Roles and Responsibilities 

As a Snowflake Data Engineer, you will design, build, and operate enterprise-grade data platforms on Snowflake. You will develop secure, governed, and cost-efficient data pipelines and models; enable analytics and AI/ML use cases using Snowflake core services and Snowflake Cortex AI; and partner with business stakeholders to deliver reliable, high-performance data capabilities. The role spans ingestion, transformation, data architecture, performance tuning, security, and observability—anchored in Python, SQL, and agentic development patterns.

Preferred Skills

• Education: Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Information Systems, or related technical discipline

• Experience: 5+ years in data engineering with modern data warehousing; 3+ years hands-on with Snowflake (enterprise implementations)

• Technical proficiency:

• Strong Python and SQL (query optimization, UDFs, stored procedures)

• Background in agentic development (designing LLM/agent workflows, orchestration, prompt engineering, tool/function calling, evaluation)

• Data architecture/platform enablement with Snowflake (account/org setup, multi-DB design, RBAC, data sharing, governance)

• Data warehouse architectures, dimensional/data vault modeling, ETL/ELT patterns, and CI/CD for data

• Building metrics layers and KPI dashboards with underlying semantic/data models

Snowflake platform skills:

• Core: Warehouses, Databases/Schemas, Stages, Snowpipe, Tasks, Streams, External Tables

• Performance and cost optimization (clustering, micro-partitions, query profiling, warehouse sizing, workload isolation)

• Security and governance: RBAC, masking/row-access policies, network policies, platform observability, data lineage

• Integration with cloud object stores and identity (e.g., AWS S3 + IAM roles, or Azure/GCP equivalents)

• Data visualization exposure with tools such as Tableau, QuickSight, Power BI, or Metabase

• Ability to design, implement, and maintain secure access to large datasets at scale

• Primary Roles and Responsibilities

• Architect and operationalize Snowflake environments (multi-account/org patterns, RBAC, security controls, data sharing/clean rooms)

• Define data models (dimensional/vault), data contracts, and semantic layers aligned to business KPIs

• Data Engineering and Automation

• Build resilient ELT workflows using Snowflake-native features (Snowpipe, Streams & Tasks) and orchestrators (Airflow, dbt, etc.)

• Develop high-performance SQL transformations, stored procedures (Snowflake Scripting/Python), and UDFs

• Implement CI/CD for data (versioning, testing, data quality checks, Dev/Test/Prod promotion)

• Performance, Reliability, and Cost Management

• Tune queries and storage, optimize clustering and warehouse configurations, and set workload isolation/SLOs

• Establish monitoring, alerting, and cost governance; drive continuous improvements in efficiency

• Security, Governance, and Compliance

• Enforce data privacy and access controls (masking, row access, object dependencies), and document lineage

• Implement data sharing and collaboration with strong governance, policies, and auditability

• AI/ML and Advanced Analytics on Snowflake

• Leverage Snowflake Cortex AI to analyze unstructured data and build LLM-powered apps using SQL/Python

• Implement and operationalize:

• Snowflake Intelligence for natural language Q&A across structured/unstructured data (e.g., PDFs, Salesforce)

• Cortex Search for semantic search over enterprise documents

• Cortex Analyst for conversational text-to-SQL over structured data

• Document AI for document and image extraction

• Cortex Code to accelerate development for data engineering and analytics

• Ensure all AI features run within Snowflake’s secure, governed perimeter with role-based access control

• Stakeholder Partnership

• Collaborate with business owners to translate requirements into data solutions and analyses

• Deliver metrics decks and dashboards; perform root-cause analysis and develop business cases for improvements

Other Information

Culture

• Corporate Social Responsibility programs

• Maternity and paternity leave

• Opportunities to network and connect

• Discounts on products and services

Note: Benefits/Perks listed above may vary depending on the nature of your employment with KPMG and the country where you work.

Responsibilities

As a Snowflake Data Engineer, you will design, build, and operate enterprise-grade data platforms on Snowflake. You will develop secure, governed, and cost-efficient data pipelines and models; enable analytics and AI/ML use cases using Snowflake core services and Snowflake Cortex AI; and partner with business stakeholders to deliver reliable, high-performance data capabilities. The role spans ingestion, transformation, data architecture, performance tuning, security, and observability—anchored in Python, SQL, and agentic development patterns.

Qualifications

• Education: Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Information Systems, or related technical discipline

• Experience: 5+ years in data engineering with modern data warehousing; 3+ years hands-on with Snowflake (enterprise implementations)

• Technical proficiency:

• Strong Python and SQL (query optimization, UDFs, stored procedures)

• Background in agentic development (designing LLM/agent workflows, orchestration, prompt engineering, tool/function calling, evaluation)

• Data architecture/platform enablement with Snowflake (account/org setup, multi-DB design, RBAC, data sharing, governance)

• Data warehouse architectures, dimensional/data vault modeling, ETL/ELT patterns, and CI/CD for data

• Building metrics layers and KPI dashboards with underlying semantic/data models