About the service
I build a transformation layer that turns your warehouse's raw data into analytics you can trust. Using dbt on top of Snowflake, BigQuery, Redshift, or Postgres, I design modular SQL models arranged in staging, intermediate, and marts layers, so each metric has a clear lineage from source to dashboard. Instead of chaos from scattered scheduled queries and duplicated logic, you get a versioned project where every transformation is described once, tested, and reused everywhere it's needed.
My job isn't just to write models but to make them predictable. I add data tests for uniqueness, null values, acceptable ranges, and referential integrity so broken assumptions surface before they hit reports. I document every model and column so analysts understand field meaning without extra questions. I configure incremental builds so large tables update in minutes instead of hours, and tie everything to CI: a pull request runs the full test suite and blocks anything that would push bad data to prod. The result is a pipeline that behaves like real software — testable, reproducible, and safe to change.
Whether you're starting from scratch or untangling inherited chaos, I integrate into your stack and git workflow, hand over clean documentation, and train your team so the project lives after I leave. You're left with a maintainable analytics codebase, not a black box.
— Layered dbt project: staging, intermediate, and marts
— Tests, documentation, and lineage for every model
— Incremental models and cost-optimized builds
— CI that runs tests on every pull request
My job isn't just to write models but to make them predictable. I add data tests for uniqueness, null values, acceptable ranges, and referential integrity so broken assumptions surface before they hit reports. I document every model and column so analysts understand field meaning without extra questions. I configure incremental builds so large tables update in minutes instead of hours, and tie everything to CI: a pull request runs the full test suite and blocks anything that would push bad data to prod. The result is a pipeline that behaves like real software — testable, reproducible, and safe to change.
Whether you're starting from scratch or untangling inherited chaos, I integrate into your stack and git workflow, hand over clean documentation, and train your team so the project lives after I leave. You're left with a maintainable analytics codebase, not a black box.
— Layered dbt project: staging, intermediate, and marts
— Tests, documentation, and lineage for every model
— Incremental models and cost-optimized builds
— CI that runs tests on every pull request
