Sr. Data Engineer

Key Responsibilities

• Design, build, and maintain scalable, reliable data pipelines that support analytics, reporting, and business intelligence needs
• Develop and optimize data transformation workflows using dbt, following best practices for modularity, testing, and documentation
• Architect and manage data models and warehousing solutions within Snowflake, ensuring performance, scalability, and cost efficiency
• Integrate data from multiple source systems using ETL/ELT tools, ensuring data quality, consistency, and timeliness
• Troubleshoot and resolve complex data pipeline issues, applying strong analytical and problem-solving skills
• Collaborate with data analysts, data scientists, and business stakeholders to understand requirements and translate them into robust technical solutions
• Implement data quality checks, monitoring, and alerting to ensure pipeline reliability
• Optimize SQL queries and warehouse performance (clustering, partitioning, query tuning)
• Document data architecture, pipeline logic, and processes for team knowledge sharing
• Stay current with emerging data engineering tools and practices, recommending improvements where relevant

• Design, build, and maintain scalable, reliable data pipelines that support analytics, reporting, and business intelligence needs
• Develop and optimize data transformation workflows using dbt, following best practices for modularity, testing, and documentation
• Architect and manage data models and warehousing solutions within Snowflake, ensuring performance, scalability, and cost efficiency
• Integrate data from multiple source systems using ETL/ELT tools, ensuring data quality, consistency, and timeliness
• Troubleshoot and resolve complex data pipeline issues, applying strong analytical and problem-solving skills
• Collaborate with data analysts, data scientists, and business stakeholders to understand requirements and translate them into robust technical solutions
• Implement data quality checks, monitoring, and alerting to ensure pipeline reliability
• Optimize SQL queries and warehouse performance (clustering, partitioning, query tuning)
• Document data architecture, pipeline logic, and processes for team knowledge sharing
• Stay current with emerging data engineering tools and practices, recommending improvements where relevant