We are seeking a Lead Snowflake Data Engineer to architect and optimize a scalable, secure and high-performance data warehouse solution on AWS or Azure, leveraging Snowflake and DBT.

This role is pivotal in designing robust data models, developing efficient ETL pipelines and ensuring seamless integration with monitoring and observability tools for a future-ready storage platform.

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 uphold 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 promote transparency and knowledge sharing within the team

Requirements

8 to 13 years of working experience in data engineering or related fields
Knowledge of Snowflake, DBT and either AWS or Azure
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, particularly Data Vault methodology
Capability to design and develop 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 platforms