Data engineering
Hire senior data engineers in the US & Canada
Pipelines and warehouses your team can trust.
When two dashboards disagree, people stop trusting either. Data engineers fix the plumbing behind that: how data is collected, cleaned, modelled and delivered, so decisions rest on numbers everyone believes.
We place senior data engineers who have built warehouses and pipelines in production. They care about testing, documentation and lineage as much as throughput.
What they typically own
- Setting up a warehouse in Snowflake or BigQuery with a clear modelling approach
- Building batch and streaming pipelines in Spark, Airflow and Kafka
- Using dbt to make transformations tested, versioned and documented
- Adding data quality checks and alerts before bad data reaches reports
- Controlling warehouse cost and query performance
How we vet
What we look for in data engineering engineers
On top of our standard assessment (system design, coding and references), we test the things that matter most for this role.
Modelling and SQL depth
Candidates design a model for a realistic business problem and show strong command of SQL.
Pipeline reliability
We discuss failure handling, idempotency and backfills, which reveal real production experience.
Communication with analysts
References confirm they work well with the people who use the data.
Read more in our guide: how to vet a senior engineer.
Questions
Before you hire
Which warehouse should we choose?
It depends on your existing cloud, data size and team skills. A data engineer can give a grounded recommendation during discovery.
Can a data engineer help before we hire analysts?
Yes. A solid foundation makes analysts far more productive when they arrive.
Other roles we place
Ready to meet data engineering engineers?
Tell us about your stack and timeline. We'll introduce engineers who fit, and reply within one business day.