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Job Description & How to Apply Below
This role is essential for building robust data transformation pipelines, ensuring data integrity and supporting future platform use cases.
Responsibilities
Design and implement scalable Snowflake data models tailored for data ingestion and analytics requirements
Build and maintain robust ETL pipelines to integrate data from multiple sources into Snowflake, ensuring data integrity and consistency
Optimize Snowflake usage and storage, tuning query performance and managing data partitions for reliable data access
Implement best practices in data security, role-based access control and data masking within Snowflake to maintain compliance and governance standards
Utilize dbt and workflow management tools such as Apache Airflow to schedule data processing and automate pipeline monitoring
Partner with data scientists, business analysts and other stakeholders to address complex data challenges and troubleshoot Snowflake-related issues
Develop comprehensive documentation for data structures, ETL workflows and system processes to ensure transparency and knowledge sharing
Integrate monitoring and observability capabilities into the data warehouse solution
Ensure the warehouse is reusable and supports future use cases as a storage layer for the platform
Requirements
5 to 8 years of working experience in data engineering or related fields
Knowledge of AWS or Azure, DBT and Snowflake
Proficiency in Python programming
Skills in workflow management tools such as Argo, Oozie or Airflow
Expertise in Snowflake modeling including roles, schema and databases
Background in data modeling, specifically Data Vault methodology
Experience in designing and developing data transformation pipelines using the DBT framework
Understanding of scalable, secure and high-performance data warehouse architectures
Familiarity with integrating monitoring and observability capabilities into data solutions
Position Requirements
10+ Years
work experience
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