Data Pipelines & Reporting
Decisions made on a stale export and a hand-assembled spreadsheet are decisions made on bad information that looks like good information. We build the pipelines and the reporting layer that deliver clean, current numbers to every stakeholder, on schedule, without anybody exporting anything.
One version of the truth, and it updates itself.
Bad data costs more than no data, because it produces confidence. We build extraction, transformation, and load pipelines that normalise every source into a single reporting layer, and we put anomaly detection on top of it, so a number that moves impossibly gets flagged as a data problem before someone builds a strategy on it. This layer is also what the models read from, which is why we build it before we promise anything downstream of it.
Automated ETL pipelines
Extraction from every source that matters — ad platforms, CRM, billing, product analytics — transformed to a consistent schema and loaded to your warehouse or reporting layer on a schedule or on an event.
Unified reporting layer
Marketing, sales, finance, and operations reading from the same numbers. The Monday meeting starts with the discussion instead of forty minutes spent reconciling three spreadsheets that disagree.
Anomaly detection and alerts
When a metric moves outside its expected range, you hear about it immediately. Half of those alerts will be broken pipelines and half will be real business events, and both are things you want to know on the day rather than at the end of the quarter.
- Data source audit and pipeline architecture
- ETL pipeline build (extraction, transformation, loading)
- Data warehouse or BI layer configuration
- Scheduled refresh and real-time sync setup
- Anomaly detection rules and alert routing
- Documented, version-controlled transformation logic
The engagement
Source map
Inventory every data source, every report someone assembles by hand, and every place two systems already disagree about the same number.
Transform
Design the schema and the transformation logic, then put it in version control, so the definition of a metric is a thing you can review rather than a thing you inherit.
Schedule
Ship the pipelines, wire the anomaly alerts, and hand over a reporting system that refreshes itself and complains when it cannot.
Common questions
BigQuery, Snowflake, Redshift, and Postgres. For smaller stacks, Looker Studio, Metabase, or even a well-structured Notion database can be the right answer, and we would rather right-size this than sell you a warehouse you will not fill.
That is expected, and it is most of the work. Transformation logic exists precisely to normalise inconsistent source data into something queryable. What we cannot fix is data that was never captured, and we will tell you early if that is what we have found.