Data Engineer - US Remote

PerfectServe’s mission is to accelerate speed to care by optimizing provider schedules and routing communications — including messages, pages, calls, and alerts — to the right place at the right time in any care setting. By facilitating real-time information sharing, building better schedules, and automating importa...

Leading analyst firms like Gartner® and KLAS Research have consistently validated our approach:

In 2026, PerfectServe was named highest in execution and furthest in vision in the Gartner Magic Quadrant™ for Clinical Communication and Collaboration — the clear segment leader.

Join a new, small team building PerfectServe's data platform.
We are building the pipelines and models that bring data together from across PerfectServe's product portfolio. You will work closely with a staff data engineer and a platform engineer, with enough ownership to see your work in production and enough support to learn quickly.
The source systems are varied, so the work spans extending established pipelines and designing new ones from scratch. That range is a good part of the job.
Joining a platform in its first months is a different experience from joining one that is five years old. The patterns are still being set, which means your questions change them. You will see the whole path — a production source system at one end, someone making a decision at the other — rather than one layer of a stack somebody else assembled.
You will have a dedicated team to work with and learn from so the ownership comes without being thrown in unsupported.
Build and maintain ingestion pipelines from production source systems
Write transformation models that turn raw data into datasets people can trust
Apply dimensional and medallion modelling patterns as the warehouse takes shape
Deploy pipelines to production, and test them properly — idempotency, data quality checks
Contribute to data model and schema design, and to design reviews
Document what you build, including runbooks for the pipelines you own

Professional experience building data pipelines, with production ownership of at least some
Strong SQL, and a working programming language — Python, Go or equivalent
Hands-on dbt, or another transformation framework
A cloud data warehouse — Snowflake, BigQuery, Redshift or similar
Practical ELT and orchestration experience (Fivetran, Airflow or equivalent)
Working AWS knowledge — object storage, access basics, and enough networking to reason about where data moves
A testing instinct for data, not only for code
Change-data-capture
SQL Server sources
Healthcare or another regulated data environment
Exposure to a BI or semantic layer