We are seeking an experienced Lead DBT Developer with strong hands-on expertise in DBT, Snowflake, and advanced SQL to lead the design, development, and optimization of modern data transformation and ELT solutions.
The ideal candidate will have a strong background in data engineering and data warehousing, with proven experience developing scalable DBT projects, implementing data quality frameworks, optimizing Snowflake workloads, and establishing engineering best practices. This role will also provide technical leadership through design and code reviews, mentoring engineers, managing technical risks and dependencies, and collaborating with product and business stakeholders to drive the data roadmap.
Roles and Responsibilities
- Design, develop, and optimize scalable data models and transformation pipelines using DBT and Snowflake.
- Build and manage ELT pipelines using DBT and other data engineering technologies.
- Develop and maintain DBT projects using DBT models, Jinja macros, configurations, packages, and reusable frameworks.
- Implement DBT testing, data validation, documentation, lineage, and data quality best practices.
- Write and optimize complex SQL queries for large-scale data processing and analytics workloads.
- Leverage a strong understanding of Snowflake architecture to improve query performance, scalability, reliability, and cost efficiency.
- Design and implement robust data warehouse solutions aligned with enterprise data engineering and modeling best practices.
- Troubleshoot data pipeline, transformation, and data quality issues and drive timely resolution.
- Establish and maintain coding standards, development practices, and technical best practices for DBT projects.
- Conduct architecture, design, and code reviews, ensuring solutions are scalable, maintainable, and aligned with organizational standards.
- Mentor and coach data engineers, helping improve technical capabilities and engineering practices.
- Manage technical risks, dependencies, and delivery challenges across data engineering initiatives.
- Collaborate closely with data engineers, analysts, product managers, business stakeholders, and other technology teams to understand requirements and deliver effective data solutions.
- Contribute to data architecture decisions and help define the technical roadmap for modern data platforms.
- Support and maintain Git-based version control, CI/CD pipelines, and deployment processes for DBT projects.
- Develop and maintain comprehensive technical documentation for data models, transformations, pipelines, and processes.
- Work effectively within an Agile/Scrum environment, participating in planning, refinement, estimation, and delivery activities.
- Stay current with emerging technologies, tools, and best practices across DBT, Snowflake, cloud data platforms, and modern data engineering.
Required Technical Skills
- Strong hands-on experience with DBT (Data Build Tool) in enterprise data engineering environments.
- Strong experience with Snowflake and its architecture, including performance and cost optimization.
- Advanced SQL skills, including complex queries, CTEs, window functions, performance tuning, and data transformation techniques.
- Strong understanding of data warehousing concepts, dimensional modeling, ELT/ETL processes, and data transformation.
- Experience developing and managing DBT projects, including:
- DBT models and transformations
- Jinja templates and macros
- Configurations and packages
- Sources and snapshots
- Tests and data quality frameworks
- Documentation and lineage
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Experience with Git and version control.
- Understanding of CI/CD practices and automated deployment pipelines for data engineering projects.
- Experience with workflow orchestration or data pipeline management tools is preferred.
- Strong troubleshooting, analytical, and problem-solving skills.
Nice to Have
- Experience in Property & Casualty (P&C) Insurance or broader insurance industry data environments.
- Experience with coaching, facilitation, and technical team enablement.
- Experience working with enterprise-scale data platforms and complex data ecosystems.
- Exposure to modern data architecture and cloud-native data engineering practices.