Snowflake & dbt Developer Needed
Budget: ₹750 – ₹1,250 INR
I am midway through a data-warehouse build and now need a developer who genuinely knows their way around both Snowflake and dbt. The project is already in the development and implementation phase; the core schemas are in place, code repositories are set up, and the deployment pipeline is running. What I need is someone who can jump straight in, extend the existing models, and tighten everything up for production.
Your day-to-day focus will be on:
• Data modeling and transformations – extending star/snowflake schemas, writing efficient dbt models in SQL/Jinja, and making sure tests pass.
• Data pipeline setup and maintenance – adding new sources, configuring incremental loads, scheduling runs in dbt Cloud, and integrating with our existing Git-based CI/CD.
• Query optimization and performance tuning – clustering, partitioning, and leveraging Snowflake features such as result caching and query profiling to keep costs predictable and latency low.
I already have access to secure development and production Snowflake accounts, a staging environment in dbt Cloud, and a backlog of user stories. You’ll be given branch access, sample data, and detailed acceptance criteria for each story, including run-time targets and test coverage requirements.
Deliverables I will review for sign-off:
1. Updated dbt project repository with new or refactored models, seeds, snapshots, and tests.
2. Documented run results proving jobs complete within agreed performance thresholds.
3. A concise “runbook” covering pipeline orchestration steps, alerts, and any Snowflake warehouse settings you adjust.
If you have solid, hands-on experience with Snowflake warehouses and advanced dbt features (macros, exposures, semantic layer), plus a knack for writing clean, well-tested SQL, let’s talk.
Your day-to-day focus will be on:
• Data modeling and transformations – extending star/snowflake schemas, writing efficient dbt models in SQL/Jinja, and making sure tests pass.
• Data pipeline setup and maintenance – adding new sources, configuring incremental loads, scheduling runs in dbt Cloud, and integrating with our existing Git-based CI/CD.
• Query optimization and performance tuning – clustering, partitioning, and leveraging Snowflake features such as result caching and query profiling to keep costs predictable and latency low.
I already have access to secure development and production Snowflake accounts, a staging environment in dbt Cloud, and a backlog of user stories. You’ll be given branch access, sample data, and detailed acceptance criteria for each story, including run-time targets and test coverage requirements.
Deliverables I will review for sign-off:
1. Updated dbt project repository with new or refactored models, seeds, snapshots, and tests.
2. Documented run results proving jobs complete within agreed performance thresholds.
3. A concise “runbook” covering pipeline orchestration steps, alerts, and any Snowflake warehouse settings you adjust.
If you have solid, hands-on experience with Snowflake warehouses and advanced dbt features (macros, exposures, semantic layer), plus a knack for writing clean, well-tested SQL, let’s talk.