Data Engineer
Build the data platforms decisions actually get made on — pipelines, warehouses and models that hold up under operational load.
About the role
Clients usually come to us with data spread across systems that were never meant to talk to each other, and a reporting layer nobody trusts. The job is to make the numbers reliable enough that people act on them.
You will design and build the pipelines and warehouse models underneath that, working with whatever stack the client already has rather than insisting on a rewrite.
Trust is the actual deliverable. A dashboard nobody believes is worse than no dashboard, so testing, lineage and documentation are part of the work rather than a later phase.
What you'll do
- Design and build batch and streaming pipelines from source systems to the warehouse
- Model data for analytics — dimensional or otherwise, as the use case warrants
- Implement data quality testing, lineage and observability
- Work with client analysts to make sure what you build is what they actually needed
- Document models and pipelines so the client's team can own them afterwards
What we're looking for
- Four or more years in data engineering
- Strong SQL and Python
- Experience with a modern warehouse — Snowflake, BigQuery, Databricks or Redshift
- Orchestration tooling such as Airflow, Dagster or dbt in production use
- Understanding of data modelling trade-offs, not just the tools
- Authorised to work in the United States without sponsorship
Nice to have
- dbt at scale
- Streaming — Kafka, Kinesis or equivalent
- Data governance or privacy-constrained environments
- Analytics engineering or BI background
Not sure you tick every box?
Apply anyway. The requirements above describe the shape of the role, not a checklist we score against. We would rather read your application and decide than have you rule yourself out.
Apply for Data Engineer
Takes a few minutes. You'll get an email confirming we have it, and if your résumé is shortlisted someone from our team will contact you directly.
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