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Bloodcuff

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Data Platform Engineer

SeniorRemote (US)$165k–$195k + equity

You’ve applied to Bloodcuff. The hiring team has asked you to take the next step here: an evaluation run by Evidal — a conversation about real work you’ve done, not a form. What you say goes to the hiring team as a structured read of your experience.

This isn’t a resume. It’s a conversation about real work you’ve done — not bullet points, not titles, not buzzwords. Every evaluation runs the same way, for everyone. Nobody is filtered out by keywords. Learn more ↗

Proceed with Evaluation →What you’ll be asked about

Data Platform Engineer

Bloodcuff's blood-pressure monitors stream readings from tens of thousands of devices into our cloud platform. As our Data Platform Engineer you will own the pipelines that land, validate and serve that clinical-grade time-series data: ingestion from device fleets, warehouse modeling for product and regulatory analytics, data quality contracts, and the serving layer our ML and dashboard teams build on.

Requirements

  • •5+ years building production data pipelines in Python and SQL
  • •Streaming ingestion at scale (Kinesis, Kafka or similar)
  • •Warehouse modeling with dbt or similar on Snowflake/BigQuery/Redshift
  • •Owned reliability of scheduled + streaming jobs (SLOs, on-call)
  • •Data quality contracts and lineage for audited environments

Nice to have

  • •Healthcare or regulated-data experience
  • •Cost optimisation of warehouse workloads
  • •Terraform/IaC for data infrastructure

What this role needsWhat you’ll be asked about.

Read it before you start. Each one says why this team asks for it and what kind of story lands.

Required experience

Be ready to discuss a real project for each — one project can cover several.

Every one of these comes up.

01

Python

Bring a project

Stated as the primary implementation language for pipeline and data engineering work.

Written by the hiring team for this role.

How the team reads this

Building and maintaining ingestion, transformation, and serving pipeline logic day-to-day.

02

SQL and warehouse modeling

Bring a project

Warehouse modeling for product and regulatory analytics is an explicit responsibility.

Written by the hiring team for this role.

How the team reads this

Writing dbt models, authoring transformations in Snowflake or equivalent, and structuring analytical schemas.

03

Streaming and batch pipeline engineering

Bring a project

Ingestion from device fleets via streaming (Kinesis) and nightly batch jobs are core responsibilities.

Written by the hiring team for this role.

How the team reads this

Designing, operating, and debugging Kinesis-based streaming pipelines and scheduled batch workflows.

04

AWS cloud data platform

Bring a project

The full stack runs on AWS; services like Kinesis, S3, and surrounding infra are the operating environment.

Written by the hiring team for this role.

How the team reads this

Configuring and operating S3 storage, Kinesis streams, IAM, and adjacent AWS primitives for data workloads.

Any of: Kinesis · S3 · Glue · Lambda · EMR

05

Data quality contracts

Bring a project

Owning data quality contracts is an explicit stated responsibility critical to clinical-grade data.

Written by the hiring team for this role.

How the team reads this

Defining, enforcing, and monitoring schema and value-level contracts across pipeline stages.

Strongly preferred experience

An example you can talk through is enough.

These come up when there is time — and “not yet” is a real answer.

06

dbt or comparable transformation framework

Bring a project

dbt is called out as the transformation layer for warehouse modeling.

Written by the hiring team for this role.

How the team reads this

Authoring and maintaining dbt models, tests, and documentation for the analytical warehouse.

Any of: dbt · SQLMesh · Dataform

07

Data lineage for regulated environments

Bring a situation

Partnering with compliance teams on data lineage for audits is explicit; clinical-grade context raises the stakes.

Written by the hiring team for this role.

How the team reads this

Documenting and surfacing lineage metadata to satisfy regulatory audit requirements in coordination with compliance and firmware teams.

How to prepare

  • Have a few real projects in mind that you were involved in and that connect to what this role needs.
  • Know the boundary of what you did — what you built, what your team built, what you inherited.
  • Bring the decision, not only the outcome: what you chose, and what you gave up.
  • Say plainly where something did not work. Honest limits read better than a clean story.

Ready to get started?Two ways to apply — pick the one that fits.

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skill.md ↗

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https://evidal.ai/api/v1/company/bloodcuff/roles/data-platform-engineer/skill.md

Or paste this into your agent:

Read https://evidal.ai/api/v1/company/bloodcuff/roles/data-platform-engineer/skill.md and apply to this role on my behalf. Use what you already know about my work, and ask me for anything you're missing.