Snowflake Developer

About Artiminds

Data & AI, Cloud, Microsoft Power Platform, SAP, and Aerospace Engineering & Enterprise services.

Role Summary

Snowflake Architect

Key Responsibilities

• Define and own the end-to-end architecture of data platforms built on Snowflake, covering ingestion, transformation, storage, and consumption layers.
• Design and implement

  data lakehouse architectures

  using Snowflake's native features (Streams, Tasks, Materialized Views, External Tables).
• Establish best practices for

  data governance, security, and access control

  (RBAC, Secure Views, Row-Level Security).
• Architect scalable ETL/ELT pipelines using Snowflake, DBT, and Python, integrating with orchestration tools like Airflow.
• Integrate Snowflake with enterprise systems such as data catalogs, data quality frameworks, monitoring tools, and BI platforms.
• Enable

  AI integrations with Snowflake

  , including Snowflake Cortex and LLM-powered analytics.
• Hands-on experience with

  Snowflake Horizon

  governance features (object tagging, data lineage, Data Clean Rooms, AI-powered insights).
• Guide teams in implementing CI/CD pipelines for Snowflake deployments (GitHub Actions, Azure DevOps, GitLab CI).
• Monitor and optimize Snowflake performance using Query Profile, Resource Monitors, Clustering Keys, and Auto-Suspend/Resume.
• Collaborate with data analysts, engineers, and business stakeholders to ensure platform reliability and data accessibility.
• Conduct architectural reviews, code audits, and mentoring sessions to ensure adherence to standards and scalability.
• Stay current with Snowflake innovations and advocate for adoption of new features and capabilities.

Skills & Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, or related field.

• 5+ years

  of experience in data development and solutions within large, complex data environments.

• 5+ years

  of SQL/PLSQL experience, including writing complex queries for data analysis.

• 5+ years

  developing data pipelines and data warehousing solutions using Python (Pandas, NumPy, etc.).
• Strong knowledge of Snowflake architecture (virtual warehouses, storage layers, compute scaling).
• Hands-on DBT experience preferred.
• Proficiency in performance tuning of SQL queries, Spark jobs, and stored procedures.
• Solid understanding of E-R data models (conceptual, logical, physical) and advanced data warehouse concepts.
• Strong analytical, problem-solving, and communication skills with the ability to collaborate across IT and business groups globally.