ETL Developer

Overview

Design, build and maintain scalable data pipelines and transformation models on Snowflake and Azure using dbt and advanced SQL.

What you'll do

• Design, develop and maintain scalable data pipelines using Snowflake, Azure and dbt.
• Develop complex data transformation and business logic using dbt models and advanced SQL.
• Build and optimize Snowflake objects including databases, schemas, tables, views and other data-processing components.
• Develop data ingestion and integration solutions for loading data from multiple upstream systems into the Snowflake platform.
• Implement incremental processing, reusable transformation models and dependency-driven workflows using dbt.
• Develop automated data-quality checks, reconciliation and validation controls to ensure completeness and accuracy of reporting data.
• Optimize SQL queries and Snowflake workloads for performance, scalability and efficient resource utilization.
• Work with Azure-based data services and orchestration frameworks supporting ingestion, transformation and downstream delivery.
• Troubleshoot data-quality, pipeline and production-processing issues and perform root-cause analysis.
• Support development, testing, release and production implementation activities.
• Work closely with business analysts, application teams and downstream reporting teams to translate requirements into technical solutions.
• Maintain technical documentation covering data flows, transformations, dependencies and operational procedures.
• Follow appropriate Git-based version control, code review and CI/CD practices for dbt and data engineering components.
• Participate in production support and ensure timely resolution of batch and data-processing failures.

What you'll need

• Strong hands-on experience with Snowflake Data Cloud.
• Advanced Snowflake SQL and data transformation experience.
• Experience working with tables, views, stages, file formats and data-loading patterns.
• Good understanding of Snowflake performance optimization and warehouse/resource management.
• Experience troubleshooting large-volume data-processing workloads.
• Strong hands-on development experience with dbt.
• Experience developing reusable dbt models using sources, staging and transformation models, incremental models, and macros/Jinja.
• Strong understanding of Azure-based data engineering architecture.
• Experience building or supporting automated ingestion and orchestration workflows.
• Strong SQL development and performance-tuning skills.
• Experience handling high-volume datasets and complex transformations.
• Strong understanding of data modeling.
• Strong understanding of data-quality validation.