Analytics engineering with dbt: trusted transformation layer

I build the transformation layer that turns your raw warehouse data into analytics you can actually trust. Using dbt on top of Snowflake, BigQuery, Redshift or Postgres, I design modular SQL models organized into staging, intermediate and marts layers, so every metric has a clear lineage from source to dashboard. Instead of a tangle of scheduled queries and copy-pasted logic, you get a versioned project where each transformation is defined once, tested, and reused everywhere it is needed.

My work is not just writing models, it is making them dependable. I add data tests for uniqueness, not-null, accepted values and referential integrity, so broken assumptions surface before they reach a report. I document every model and column so analysts understand what a field means without asking. I configure incremental builds so large tables refresh in minutes rather than hours, and I wire everything into CI, where a pull request runs the full test suite and blocks anything that would ship bad data. The result is a pipeline that behaves like real software: reviewed, reproducible and safe to change.

Whether you are starting from scratch or untangling an inherited mess, I fit into your stack and your git workflow, hand over clean documentation, and coach your team so the project keeps living after I leave. You end up owning a maintainable analytics codebase, not a black box.

— Layered dbt project: staging, intermediate and marts
— Tests, documentation and data lineage for every model
— Incremental models and tuned, cost-aware builds
— CI pipeline that runs tests on every pull request