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Snowflake Architect

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Hybrid

Snowflake Architect

Mastech Digital

Snowflake Architect

Skills

Google Cloud PlatformSQL QueriesLookerData EngineeringTableauPythonKafkaSnowflakeDatabricksAWSdbtOracleCompliancePower BIAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

24 August 2026

What they need


Role Overview

We are looking for a seasoned Snowflake Architect to design, implement, and optimize enterprise data warehouse solutions on Snowflake. The ideal candidate will have deep expertise in cloud data platforms, strong architectural skills, and proven experience in leading large-scale data transformation initiatives.

Key Responsibilities

  • Define and own the end-to-end Snowflake architecture for enterprise data platforms.
  • Design scalable data models, schemas, and pipelines aligned with business requirements.
  • Establish best practices for performance tuning, security, and cost optimization in Snowflake.
  • Lead migration strategies from legacy data warehouses (Teradata, Oracle, Redshift, etc.) to Snowflake.
  • Collaborate with stakeholders to translate business needs into technical solutions.
  • Integrate Snowflake with ETL/ELT tools, BI platforms (Tableau, Power BI, Looker), and cloud services.
  • Provide technical leadership and mentorship to data engineers and developers.
  • Ensure compliance with data governance, privacy, and regulatory standards.

Required Skills & Experience

  • Strong expertise in Snowflake Data Warehouse (architecture, security, optimization).
  • Hands-on experience with SQL, Python, DBT, and ETL/ELT frameworks.
  • Deep knowledge of cloud platforms (AWS, Azure, GCP) and their data services.
  • Experience in data modeling, schema design, and metadata management.
  • Exposure to data governance frameworks and compliance requirements.
  • Strong communication and stakeholder management skills.

Preferred Qualifications

  • Snowflake Architect certification(s).
  • Experience with Databricks, Kafka, or other modern data engineering platforms.
  • Prior experience in large-scale enterprise data transformations.

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