Snowflake Data Engineer

Role & responsibilities

• Expertise in 

  Snowflake

  platform implementation and management

   within complex data environments.
• Strong understanding of 

  Data Warehousing concepts

   and 

  RDBMS

  .
• Ability to evaluate data systems for 

  variances, discrepancies, data quality, and overall efficiency

  .
• Proficient in 

  troubleshooting and optimizing

  Snowflake-based data systems

   to improve performance and scalability.
• Skilled in translating complex business requirements into 

  scalable

  Snowflake

  architectures and data solutions

  .
• Experience collaborating with 

  business stakeholders and cross-functional teams

  .
• Strong communication and requirement-gathering skills.
• Familiarity with 

  performance tuning, data integrity, and

  Snowflake

  best practices

  .
• Experience with 

  dbt

   for data transformation, modeling, testing, and deployment is required.

Preferred candidate profile

• Lead the 

  design and implementation of

  Snowflake

  data solutions

   aligned with business requirements.
• Translate complex data requirements into effective 

  Snowflake

  architecture, data models, and workflows

  .
• Develop and maintain scalable 

  dbt

  models, transformations, tests, and documentation

  .
• Provide technical guidance and support to 

  ETL developers

   in developing accurate 

  High-Level Design (HLD)

   and 

  Low-Level Design (LLD)

   data mappings.
• Monitor and analyze data-system performance and proactively identify and resolve issues.
• Perform 

  Snowflake

  performance tuning

  , query optimization, warehouse optimization, and scalability improvements.
• Evaluate implemented data solutions to ensure 

  data integrity, quality, efficiency, and reliability

  .
• Collaborate with cross-functional teams to gather requirements and deliver optimal data solutions.
• Participate in 

  requirement analysis, data mapping, solution design, and acceptance testing

  .
• Ensure adherence to 

  Snowflake,

  dbt, data engineering, development, and deployment best practices

  .
• Troubleshoot data discrepancies and investigate issues across source, transformation, and target layers.
• Contribute to continuous improvement of Snowflake and dbt development standards and processes.