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Consultant - Snowflake Data Engineer

KPMG Global Services
Bengaluru, IND
Full-time
Mid · 5+ years experience
Onsite
Discovered 1 weeks ago
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Full Job Posting

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.
  • 5+ years in data engineering with modern data warehousing; 3+ years hands-on with Snowflake (enterprise implementations)
  • 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
  • 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
  • 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
  • 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
  • Number of interview rounds
  • 2
  • Mode of interview
  • Virtual
  • Job location
  • Bangalore/Pune/Gurgaon
  • Clean room policy (specific to business)
  • 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.
  • Education: Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Information Systems, or related technical discipline

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
  • Number of interview rounds
  • 2
  • Mode of interview
  • Virtual
  • Job location
  • Bangalore/Pune/Gurgaon
  • Clean room policy (specific to business)
  • 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.

Qualifications

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

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.

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