Data Pipelines & ETL
Data that arrives on time, every time.
Ad platforms, analytics, CRM, billing, support and your product database, pulled into the warehouse on a schedule and transformed into models the business can read. Ingestion is idempotent, transformations are tested before they land, schema changes upstream raise an alert instead of silently emptying a column, and every run is logged — so a missing number has a cause you can point at.
Data Pipelines & ETL pulls ad platforms, analytics, CRM, billing, support and your product database into the warehouse on a schedule. Loads are incremental and idempotent with safe backfills, transformations are version-controlled and tested before they land, and freshness, volume and schema-drift alerts fire before a broken field reaches a report.
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The tension
Half the pipeline is a managed connector, half is a script someone wrote two roles ago, and nobody finds out a source stopped delivering until a chart goes flat on a Monday. Every fix is a manual backfill, and every backfill risks double-counting the days it overlaps.
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The shift
One pipeline with one set of rules: incremental, idempotent loads that can be re-run safely, transformations version-controlled and tested against expectations, upstream schema drift detected on arrival, and freshness and row-count checks that fire before anyone opens a dashboard. Backfills are a command, not an afternoon.
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The outcome
Numbers that are as current as they claim to be, failures that are noticed by the pipeline rather than by your CFO, and a stack where adding the next source is a day of work instead of a project.
What you get
Concrete, not fluff.
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Ingestion from every source in your stack
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Incremental, idempotent loads with safe backfills
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Version-controlled, tested transformations
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Freshness, volume and schema-drift alerting
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Run history and lineage you can inspect
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Documented models, handed over
How it works
Brief in, live campaign out.
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Inventory every source
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Ingest incrementally
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Transform and test
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Alert on freshness and drift
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Document and hand over
It loops — every cycle feeds the next.
The proof
The pipeline should notice before you do.
Scheduled runs completing, a schema change upstream raising an alert, and a backfill closing the gap it left.
A media business lost eleven days of one channel’s spend when the platform renamed a field. Nobody noticed until the monthly review, and the blended cost figure had been wrong in two board updates by then. Drift alerting turns that class of failure into a message the same morning. Directional, not a named client.
FAQ
Frequently asked questions
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Buy where a reliable connector exists, build where it does not. Paying for a maintained connector to a major ad platform is cheaper than owning that maintenance; writing a small extractor for your own product database is usually the opposite.
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The load fails loudly or drift alerting fires, and the fix is ours. The alternative — a column that quietly turns to nulls — is the failure mode this service exists to remove.
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Most accounts run hourly, some run near-real-time where a decision depends on it, and a few sources only publish daily no matter what you pay. We will tell you which is which rather than promising a freshness the source cannot support.
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Yes, and they should be able to. Transformations live in version control with tests, so a change is reviewed like code rather than typed into a production console at midnight.
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No. The pipeline runs in your infrastructure, the code is in your repository and the models are documented. Handover is a normal ending, not a penalty clause.