Data pipeline consulting for teams tired of babysitting scripts
Every company has the same origin story: someone wrote a script to pull data from an API into a spreadsheet, it worked, and three years later that script — and forty of its cousins — silently run the company’s reporting. Nobody knows all of them. Some fail quietly. The dashboard looks fine until a number is wrong in front of the CFO.
DEV3LOPCOM, LLC is a 100% onshore consultancy in Austin, Texas that replaces that pile with pipelines built like production software: scheduled, monitored, retryable, documented, and owned. Our founder is a former Tableau Software Professional Services consultant, so we build pipelines with the destination in mind — the dashboard, the model, the operational system — not as plumbing for its own sake.
How an engagement runs
We start by mapping what exists: every source, every script, every schedule, every consumer. That inventory alone is usually worth the engagement — at Lever it covered 282 SQL queries, 186 Tableau workbooks, and 11 Python apps, and consolidating it saved an estimated 22,000 hours per year. From the inventory we design the target architecture: which sources feed which models, ELT versus ETL for each flow, incremental versus full loads, where orchestration lives, and what alerting looks like. Then we build in increments — the most fragile or most valuable flow first — and hand off with runbooks, tests, and a working session so your team can extend the system without us.
The failures we engineer out
- Silent failures. A cron job that dies with no alert is worse than no job at all, because everyone still trusts the data. Every pipeline we ship reports success, failure, and — critically — row counts, because “succeeded but loaded zero rows” is the failure mode that burns you.
- No backfills. When logic changes or a source was down for a day, you need to reprocess history without hand-editing dates in a script. We build idempotent, parameterized loads from the start.
- Schema drift. Upstream APIs and databases change without notice. We detect drift at the boundary and fail loudly there, instead of letting nulls propagate into finance reports.
- Full reloads that grew until they no longer fit the night. Incremental loading with proper watermarking fixes the window and cuts warehouse cost at the same time.
- Credentials in code. We move secrets into a proper manager and scope access per pipeline.
Proof at scale
This is the core of what we do, at every size. For Nielsen we worked on pipelines processing trillions of records. For ExxonMobil we delivered streaming data feeding occupancy dashboards across 80+ campuses. For Buxton we built GCP pipelines supporting more than 100 analyst workbooks. The destinations vary — Redshift, Snowflake, PostgreSQL and Neon, BI platforms — but the discipline is the same: reliable movement, validated data, visible failures.
Where to start
You don’t need a platform rebuild to start. Most clients begin with one painful flow — the one that breaks monthly or the one nobody dares touch — and we make it boring: scheduled, monitored, documented. Then we repeat. If your team spends more time re-running scripts than using the data they produce, contact us and tell us about the flow that hurts most.