15-18 years of experience in data architecture and engineering
Expertise in Snowflake architecture and engineering
Advanced proficiency in SQL and Python
Deep understanding of ETL pipeline development
Experience with Snowflake Streams for real-time data ingestion
Proficiency in Snowflake's Time Travel and Fail Safe features
Bachelor's degree in a related field, with relevant certifications preferred
Responsibilities
Design and architect scalable and secure Snowflake data solutions
Develop and implement robust ETL pipelines using Snowflake Streams, Python, and SQL
Optimize Snowflake environments for performance and reliability
Lead the modeling and structuring of complex Snowflake data warehouses
Configure and manage SnowSQL and stored procedures for automation
Collaborate with teams to define data platform requirements
Troubleshoot and resolve architectural issues within Snowflake
Mentor junior architects and engineers on Snowflake best practices
Benefits
Opportunities for professional development and certification
Collaborative work environment with cross-functional teams
Access to cutting-edge data technologies
Flexible work arrangements
Potential for career advancement within the organization
Full Job Description
Principal Architect Primary Skills
DataStream, ETL Fundamentals, SQL, SQL (Basic + Advanced), Python, Data Warehousing, Time Travel and Fail Safe, Snowpipe, SnowSQL, Modern Data Platform Fundamentals, Data Modelling Fundamentals, PLSQL, T-SQL, Stored Procedures
Specialization
Snowflake Engineering: Principal Data Architect
Job requirements
15 to 18 years of experience in data architecture and engineering, including extensive expertise in Snowflake and modern data platforms
Key Responsibilities:
Design and architect scalable, secure, and high-performance Snowflake data solutions to drive enterprise data initiatives.
Develop and implement robust ETL pipelines using Snowflake Streams, Python, and SQL to enable efficient data ingestion and transformation.
Optimize Snowflake environments for performance, cost, and reliability by leveraging features such as Time Travel, Fail Safe, and Snowpipe.
Lead the modeling and structuring of complex Snowflake data warehouses, ensuring data integrity, accessibility, and scalability.
Configure and manage SnowSQL, stored procedures, and advanced SQL scripts to automate data processing and analytics workflows.
Collaborate with cross-functional teams to define data platform requirements and deliver modern data solutions aligned with business objectives.
Troubleshoot and resolve architectural and operational issues within Snowflake and related data platforms, ensuring minimal downtime.
Mentor and guide junior architects and engineers on best practices in Snowflake engineering and data platform fundamentals.
Required Skills:
Expertise in Snowflake architecture and engineering
Advanced proficiency in SQL
Strong experience with Python for data processing and automation
Deep understanding of ETL pipeline development
Hands-on experience with Snowflake Streams for real-time data ingestion
Proficiency in Time Travel and Fail Safe features within Snowflake
Implementation and management of Snowpipe for automated data loading
Skilled in SnowSQL for scripting and data management
Experience in Snowflake-specific data modeling and warehouse design
Expertise in developing and managing Snowflake stored procedures
Preferred Skills:
Experience integrating Snowflake with cloud platforms such as AWS or Azure
Expertise in advanced data security and governance practices
Knowledge of performance tuning and optimization for large-scale Snowflake workloads
Familiarity with emerging data engineering technologies and frameworks
Experience with orchestration tools for automating data pipelines (e.g., Apache Airflow, dbt)
Hands-on experience with real-time analytics and streaming data solutions
Desired Qualifications:
Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a closely related discipline
Snowflake SnowPro Advanced Architect certification or equivalent
Certification in data warehousing or cloud data platform architecture (e.g., AWS Certified Data Analytics, Microsoft Certified: Azure Data Engineer Associate)
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