Snowflake Data Architecture: Design and develop the Snowflake data architecture, including database schemas, tables, views, and other database objects. Define data modeling and integration strategies to ensure efficient and scalable data storage and retrieval.
Data Integration and ETL: Design and implement data integration processes to extract, transform, and load data from various sources into Snowflake. Utilize Snowflake's data loading and transformation capabilities, such as Snowpipe and Snowflake tasks, to automate data ingestion and processing.
Performance Optimization: Monitor and optimize the performance of Snowflake data warehouse. Analyze query performance, identify bottlenecks, and make recommendations for tuning SQL queries, optimizing data structures, and utilizing Snowflake features like clustering and partitioning.
Data Governance and Security: Establish and enforce data governance policies and practices within Snowflake. Ensure data security, privacy, and compliance with relevant regulations (e.g., GDPR or CCPA). Implement user access controls, roles, and privileges to protect data integrity and confidentiality.
Data Modeling and Design: Collaborate with business stakeholders and data analysts to understand data requirements and translate them into effective data models. Design and maintain logical and physical data models within Snowflake, ensuring alignment with business needs and best practices.
Data Quality and Integrity: Establish and implement data quality standards and processes. Develop and maintain data validation rules, automated data quality checks, and data profiling techniques to ensure the accuracy, consistency, and completeness of data stored in Snowflake.
Team Leadership and Collaboration: Lead a team of data engineers, data analysts, and other stakeholders involved in Snowflake data initiatives. Collaborate with cross-functional teams, including business analysts, data scientists, and IT teams, to align data requirements and drive data-driven initiatives.
Documentation and Reporting: Maintain comprehensive documentation of Snowflake data structures, processes, and configurations. Prepare regular reports on system performance, data quality, and usage to communicate insights and make data-driven recommendations.
Education: A bachelor's or master's degree in computer science, data management, or a related field is often preferred. Additional certifications in Snowflake or data management (such as SnowPro or CDMP) are advantageous.
Snowflake Experience: Extensive experience in working with Snowflake as a data warehouse platform. Strong knowledge of Snowflake architecture, data loading techniques, query optimization, and data governance within Snowflake.
Data Integration and ETL: Proficiency in designing and implementing data integration processes using ETL tools (e.g., Informatica, Talend) or Snowflake's native capabilities. Familiarity with data ingestion, transformation, and loading techniques.
Data Modeling and SQL: Strong skills in data modeling, including dimensional modeling and schema design. Proficiency in SQL and query optimization techniques to ensure efficient data retrieval and analysis.
Data Governance and Security: Understanding of data governance principles, data privacy regulations, and security best practices within a data warehousing environment. Familiarity with data cataloging, data lineage, and metadata management concepts.
Cloud Data Technologies: Experience with cloud-based data technologies and platforms, such as AWS, Azure, or Google Cloud. Familiarity with other data warehouse platforms, such as Redshift or BigQuery, is a plus.