Snowflake Data Modeler

Job Description:

As a Snowflake Data Modeler you will play a pivotal role in designing and optimizing data models on the Snowflake data platform. Your work will be instrumental in ensuring efficient and effective data processing, analytics, and reporting capabilities, which are essential to our data-driven initiatives.

Key Responsibilities:

• Data Modeling: Design, develop, and maintain data models that meet data processing, analytics, and reporting needs, with a specific focus on Snowflake data warehousing.
• Snowflake Expertise: Utilize your deep knowledge of Snowflake, including SnowSQL, Snowpipe, and Snowflake's unique data warehousing features, to architect data solutions.
• Performance Optimization: Identify and resolve data model performance bottlenecks, ensuring optimal data processing and query performance.
• Data Quality: Implement data quality and data governance best practices to maintain the integrity and reliability of data stored within Snowflake.
• Documentation: Create and maintain comprehensive documentation for data models, architecture, and best practices specific to Snowflake.
• Collaboration: Collaborate closely with cross-functional teams, including data engineers, data scientists, and business stakeholders, to understand data requirements and deliver high-quality data solutions.

Must have: Snowflake, Data Modelling tools like Erwin, SQL

Good to have: AWS S3, Athena, AWS Glue, Cloudera – Hive, Impala, HDFS

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.
• 8 to 12 years of experience in data modeling, with a strong focus on Snowflake.
• Proficiency in Snowflake data warehousing, including SnowSQL, Snowpipe, and Snowflake-specific features.
• Expertise in data modeling and database design concepts.
• Strong problem-solving and troubleshooting skills related to data modeling and Snowflake-specific challenges.
• Excellent communication and teamwork skills, with the ability to collaborate effectively with technical and non-technical stakeholders.
• Knowledge of data quality and data governance principles.