Rivac LabsData Engineer
Reliable analytics and downstream AI features both depend on data infrastructure that ingests, transforms, and stores data correctly and repeatably — the unglamorous plumbing that everything else quietly depends on. We build ETL and ELT pipelines and lakehouse architecture designed for your actual data volume and query patterns, not a generic reference architecture that doesn't fit your scale. This is the service when reporting is slow or wrong because of pipeline issues, or when you're scaling past spreadsheets and need a real warehouse. You get pipelines that are monitored and documented, so a schema change upstream doesn't silently corrupt six months of reports before anyone notices.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Map current data sources, volumes, and consumer needs to diagnose where the existing pipeline is breaking down.
- 2
Build a pilot pipeline for one critical data source to validate the transformation logic and warehouse schema.
- 3
Review data quality and query performance with stakeholders before extending to remaining data sources.
- 4
Roll out the full pipeline with monitoring, alerting, and documentation for ongoing operation.
What You Get
Deliverables from this engagement
- Production ETL/ELT pipelines for your data sources
- Warehouse or lakehouse schema documented and query-ready
- Data quality monitoring and alerting
- Runbook for pipeline operation and troubleshooting
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about Data EngineerMost engagements like this start as a $500–$2,500 pilot — see full pricing.