Technology
SQL, dbt & Great Expectations Testing& Development
A pipeline reports success when the job finishes, not when the data is correct, so data quality needs tooling that inspects rows rather than run status. We build reconciliation and assertion suites in SQL and Python, extend dbt tests or Great Expectations where a team already runs them, and wire the result into CI so schema drift and reconciliation failures fail a build instead of surfacing in a quarterly report.
Source-to-target reconciliation: counts, checksums, column-level comparison
dbt tests and Great Expectations extended rather than replaced
Snowflake, BigQuery, Redshift and Databricks
Schema drift detection between environments, gated in CI
Questions
Frequently Asked Questions
Straight answers, written the way we'd say them on a call.
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16+
Years QA leadership
The founder's enterprise QA career across OTT, SaaS, e-commerce and regulated utilities. Not a team total.
17
Testing disciplines
Each one has its own page, scope and deliverables. Counted from that list, never typed by hand.
6
Markets served
Availability, not delivery history. Each market's page says plainly where we have clients and where we do not.
1
Business day to reply
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Tell us where quality hurts
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