Senior DBT Developer

• 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.
• Support CI/CD pipelines for dbt projects using tools such as GitHub Actions, GitLab CI, dbt Cloud jobs, or similar deployment and automation frameworks.
• 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.
• Participate in requirement gathering sessions, technical design discussions, sprint planning, backlog refinement, walkthroughs, and UAT support activities.

• 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.
• Hands-on Snowflake experience, including databases, schemas, tables, views, warehouses, roles, access controls, query optimization, and performance considerations.
• Strong understanding of data modeling concepts, including star schema, fact and dimension tables, slowly changing dimensions, staging layers, data marts, and semantic/reporting layers.
• Experience with data reconciliation and business rule validation.
• Ability to translate business requirements, source-to-target mappings, metric definitions, and reporting logic into technical specifications and dbt transformation models.
• Good troubleshooting and analytical skills with the ability to identify root causes of data issues, failed transformations, and performance bottlenecks.
• 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.

• Experience with Tableau, including understanding dashboard requirements, published data sources, extracts, live connections, and reporting-ready data model design.
• Experience supporting BI and reporting teams by creating curated datasets, reusable views, semantic layers, and governed data marts.
• Experience with advanced dbt capabilities such as dbt Explorer, dbt Semantic Layer, dbt Mesh, exposures, metrics, and cross-project lineage is a plus.
• Experience with orchestration and scheduling tools such as Airflow, Dagster, Prefect, Azure Data Factory, Snowflake Tasks, dbt Cloud jobs, or similar platforms.
• Experience with CI/CD processes for dbt projects using GitHub Actions, GitLab CI, Azure DevOps, Jenkins, or similar automation tools.
• Experience with Python for automation, data validation, testing, metadata extraction, or data engineering support.
• Experience with ELT tools such as Fivetran, Airbyte, Matillion, Informatica, Talend, Alteryx, or similar platforms.
• Familiarity with cloud ecosystems such as AWS, Azure, or Google Cloud Platform.
• Experience with Snowflake performance optimization, warehouse sizing, clustering, query profiling, access control, monitoring, and cost optimization.
• Familiarity with Agile delivery, sprint planning, backlog management, Jira/Azure DevOps, and iterative development practices.
• Understanding of data governance, metadata management, lineage, access control, PII handling, and enterprise data standards.
• Certifications such as dbt Certified Developer, Snowflake certification, Tableau certification, or related cloud/data certifications are a plus.

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