One warehouse, a dozen sources,
kept in sync without a person watching
A business running on five platforms has its real numbers scattered across five dashboards that do not talk to each other, and reconciling them by hand every week is where good decisions get delayed. A data engineering and ETL agent builds the pipelines that pull everything, ad platforms, CRM, stores, bank exports, into one warehouse, and keeps them running: catching a schema change before it corrupts a table, re-running a failed load without creating duplicates.
The role today
A business running ads on two platforms, selling on a marketplace, and tracking orders in a CRM has, in practice, four different versions of “how many sales did we make this week,” and reconciling them by hand means pulling exports, matching rows, and hoping nothing changed format since last time. That reconciliation itself becomes a recurring task that eats the time that should go into deciding what to do with the number.
The second cost is schema drift: a source platform renames a field or changes a response format without warning, and a pipeline built to run quietly keeps running, just wrong, until someone notices a downstream number does not add up. By then the bad data may already be weeks deep in the warehouse.
The third is retry logic done badly. A failed load that simply reruns from scratch can double-count records; one that silently skips a failure loses data nobody notices is missing. Either failure mode quietly erodes trust in the warehouse, which defeats the point of building it.
What the agent takes over
The agent builds and runs the pipelines that pull data from each source on a schedule, transforms it into a consistent model, and loads it into one warehouse. It checks each source’s response against what it expects and flags schema drift immediately rather than letting a silent format change corrupt a table. Failed loads retry in a way that is idempotent, a rerun fixes the gap without duplicating what already landed correctly.
It documents the resulting data model as it builds, so a human, or an agent like the SQL analyst or BI dashboard agent downstream, can query it without reverse-engineering the pipeline first.
Typical scope: ad platforms, CRMs, e-commerce platforms, bank exports, and internal systems including older ERPs, joined into one warehouse your team or other agents can query. One of our own builds joined seven such sources, Meta, TikTok Shop, Shopify, LINE, GA4, a CRM and a factory ERP, for two brands across two countries.
What stays with humans
Deciding which sources actually matter, and resolving a genuine conflict between two sources that disagree for a real reason rather than a bug, are calls your team makes. Warehouse schema design, how the data should ultimately be modeled for your specific decisions, is a collaborative step we do with you, not something handed over blind.
Guards
Credentials to each source are scoped read-only wherever the platform allows it. A failed or skipped load always alerts rather than failing silently, data loss is never the quiet outcome. Every schema change detected is logged and flagged before it reaches any downstream table. Retries are idempotent by design, built to avoid duplicating what already loaded correctly.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Agency runs it | from $3,200 + support plan | Pipelines built, monitored and maintained by us, monthly data health review | 3 to 5 weeks |
| Full control, handover-ready | from $5,400 | Same pipelines on your own warehouse and infrastructure, documented data model, your team maintains it | 5 to 7 weeks |
Running cost is usually $30 to $120 a month in warehouse and compute costs, depending on source count and volume.
Related
See the analytics service page and AI agents service page for the surrounding build. Within this group: BI and dashboard agent, SQL analyst agent, and data quality agent are the agents that use what this one builds. For related one-time setups, see automate data quality monitoring and automate API integration. Real warehouse work behind this page: the two-brand analytics hub case study and the digital goods marketplace automation case study.
Numbers scattered across platforms that do not agree? Get in touch and we will map what you actually have first.
FAQ
How much does a data engineering and ETL agent cost?
From $3,200 for three to five data sources into one warehouse, live in 3 to 5 weeks. More sources or a more complex transformation layer usually runs $5,000 to $8,000.
How long before the warehouse is live?
3 to 5 weeks: connecting and testing each source takes most of that time, since getting the transform logic right matters more than speed. A simpler, two or three source setup can land closer to 2 weeks.
Which sources and warehouses does it work with?
Most APIs with documented access: Meta, TikTok Shop, Shopify, LINE, GA4, CRMs like HubSpot or KeyCRM, bank exports, and internal databases including older ERPs. The warehouse is usually PostgreSQL, hosted on your infrastructure.
What happens if a source changes its API or a load fails?
Schema drift is detected and alerted rather than silently breaking a downstream table. Failed loads retry automatically in a way that does not create duplicates, and if a load cannot complete, the team is alerted the same day, not whenever someone notices a number looks wrong.
Who can see and query the data once it is in the warehouse?
Access is scoped the way your team decides, typically read-only for most people, with the raw credentials to each source kept separate from general warehouse access. Everything stays on your infrastructure and under your accounts.