Privorum designs and builds data platforms, pipelines, and ingestion systems for teams that need their analytics, ML, and operational reporting to run on data they can actually trust.
We help answer practical questions such as:
- Where should the source of truth live, and which systems should read from it?
- Batch, streaming, or both — and what are the operational consequences of each?
- How do we keep pipelines observable, testable, and recoverable when something upstream changes?
- What is the right level of modelling for the warehouse, and where is it overkill?
- How do we feed ML, analytics, and product systems from one coherent data layer?
Typical engagement areas
- ingestion from APIs, databases, event streams, and third-party systems
- ETL and ELT pipelines with orchestration, retries, and lineage
- data warehousing, lakehouse design, and analytical modelling
- streaming architectures for real-time signals and event-driven products
- data quality, schema evolution, and contract testing between producers and consumers
- feature stores and training data pipelines that feed ML systems reliably
Technology depth
Python, SQL, Spark, Airflow, dbt, Kafka, PostgreSQL, BigQuery, Snowflake, and the cloud infrastructure that keeps pipelines predictable in production.
Data engineering pays off when pipelines are boring — reproducible, observable, and recoverable. We build them that way from the start.