DBT Developer

About the role

At OSI Digital Inc, we accelerate our client's digital transformation journey by delivering modern data solutions that help organizations unlock the full value of their data through scalable cloud platforms, intelligent analytics, and business-ready reporting solutions. With deep expertise across data engineering, business intelligence, cloud platforms, advanced analytics, and AI/ML, our teams bring both technical excellence and business understanding to every engagement.

Key Responsibilities

• Design, build, and maintain modular dbt models across staging, intermediate, and marts layers that transform raw data into trusted, analytics-ready datasets
• Develop and optimize dbt incremental models, snapshots, macros, seeds, sources, and reusable packages using dbt Core and/or dbt Cloud
• Write and tune complex SQL transformations in Snowflake using joins, CTEs, window functions, aggregations, conditional logic, and performance optimization techniques
• Optimize Snowflake workloads for query performance, clustering strategy, warehouse sizing, model materialization, processing time, and cost efficiency
• Implement robust data quality and testing frameworks using dbt generic tests, singular tests, custom tests, accepted values, relationships, not-null checks, uniqueness checks, and source freshness checks
• Maintain clear documentation, lineage, dependency mapping, model descriptions, column definitions, source definitions, assumptions, and business logic using dbt docs and exposures
• Manage dbt projects in Git with clean, modular, testable development practices, including branching, pull requests, code reviews, merge controls, and release promotion
• Leverage Snowflake features such as Streams, Tasks, Dynamic Tables, Time Travel, cloning, secure views, and secure data sharing to build reliable ELT workflows
• Create reporting-ready data marts, curated datasets, dimensional models, fact tables, and reusable views that support Tableau dashboards and self-service analytics
• Partner with data engineers, analysts, BI developers, architects, and business stakeholders to translate business requirements into scalable transformation logic
• Troubleshoot data discrepancies, failed dbt runs, source changes, schema changes, model errors, test failures, and performance bottlenecks
• Support CI/CD pipelines for dbt projects using tools such as GitHub Actions, GitLab CI, dbt Cloud jobs, or similar deployment and automation frameworks
• Participate in requirement gathering sessions, technical design discussions, sprint planning, backlog refinement, walkthroughs, and UAT support activities

Required Qualifications

• Minimum 4+ years of experience in data engineering, analytics engineering, BI engineering, data warehousing, or data transformation roles
• Strong hands-on experience with dbt Core and/or dbt Cloud, including dbt models, macros, tests, snapshots, seeds, sources, packages, documentation, lineage, and project structure
• Strong experience using dbt with Snowflake to build scalable ELT transformation pipelines, curated data models, and analytics-ready datasets
• Strong SQL development skills with experience writing complex transformations, performing data validation, troubleshooting data issues, and analyzing large datasets
• Strong understanding of dbt best practices for modular, testable, well-documented projects
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers
• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Analytics, Business Analytics, or a related technical field. Equivalent practical experience may also be considered