ETL consulting that treats pipelines like production software
Most ETL problems are not tool problems. They are design problems: jobs with no retry logic, transformations nobody documented, schedules that assume the source system never changes. DEV3LOPCOM, LLC is an Austin, Texas consultancy that builds extract, transform, and load workflows the way software teams build applications — version controlled, tested, monitored, and documented. We work across SQL, Python, cloud warehouses, APIs, flat files, and the SaaS ETL platforms your team already pays for, and we are 100% onshore.
If you need the broader foundation underneath the pipelines, our data engineering consulting practice covers APIs, automation, and warehouse modeling end to end.
How an ETL engagement runs
We keep the process short and visible. First, an assessment: we map every source, target, schedule, and failure mode in your current environment, then rank what breaks most often and what costs the most manual time. Second, design: target architecture, transformation logic, validation rules, and an ownership model your team can actually maintain. Third, build: we implement in small increments, with data quality checks and alerting wired in from the first job, not bolted on at the end. Finally, handoff: runbooks, documentation, and working sessions with your engineers so the pipelines do not depend on us to survive.
Common problems we fix
- Overnight batch jobs that fail silently, so the business finds bad numbers before IT does.
- Duplicate or conflicting transformation logic spread across stored procedures, scheduled scripts, and BI extracts.
- API ingestion that breaks every time a vendor changes a schema, because nothing validates payloads on the way in.
- Manual spreadsheet steps hiding inside “automated” workflows.
- Third-party ETL vendors delivering pipelines nobody in-house can read or audit.
These are the same failure patterns we have untangled at enterprise scale. At Nielsen we built pipelines handling trillions of records, where a single unvalidated edge case gets expensive fast — read the Nielsen case study. At Lever we audited and rebuilt an environment of 282 SQL queries, 186 Tableau workbooks, and 11 Python applications, work that now saves the company roughly 22,000 hours per year; details are in the Lever engagement writeup.
Why dev3lop for ETL work
Our founder came out of Tableau Software’s Professional Services organization, which means our pipelines are designed backward from the analytics they feed. Data that lands in a warehouse but cannot be trusted in a dashboard is not done. That is also why ETL engagements here often pair with Tableau consulting or data warehousing work — the transformation layer and the reporting layer get designed together instead of thrown over a wall.
We are deliberately small and senior. You work directly with the people writing the code, and every deliverable comes with documentation and training because the goal is your team running the system without a retainer.
Start with a pipeline review
The fastest way to scope ETL work is to look at what you have. Send us a description of your sources, targets, and the jobs that hurt the most, and we will come back with a concrete assessment plan. Contact us to set up a working session.