Most data platforms are built for the data a company had at the time, not the data it is about to get. Two years later the pipelines are brittle, nobody trusts the numbers, and every new question means another one-off extract.
We design the platform around your actual workloads — the queries your analysts really run, the volumes you really ingest, the latency your business really needs — using lakehouse and warehouse architectures on Databricks, AWS, and Google Cloud, with streaming ingestion through Kafka and Confluent, orchestration in Airflow or Temporal, and transformation in dbt and Spark.
What we build
- Lakehouse or warehouse architecture sized to your workloads, not to a reference diagram
- Tested, monitored ELT pipelines that fail loudly instead of silently
- Batch and real-time ingestion patterns that hold up as volume grows
- Data models your analysts can query without asking an engineer first
Migration and enablement
A platform nobody adopts is an expensive rewrite. Every build engagement includes the work that makes it stick: a migration roadmap sequenced by business value rather than technical tidiness, self-service BI rollout on curated datasets, documentation people actually consult, and hands-on training so your engineers extend the platform themselves instead of calling us for every change.
Engagement shape
We start with a two-week architecture review — current state, real costs, the three things breaking most often — and come back with a sequenced plan you can execute with or without us.
Ready to talk it through?
Tell us where your data hurts — we'll tell you honestly whether we can help.
Contact Us