Data Engineer Expert
Ethos • Remote
Education
Not stated
Type
Hourly
Pay
$80/hr
The rate in the listing.
Listed
8d ago
Checked
- 4+ years as a software engineer, data engineer
- Direct experience evaluating model outputs or system readiness and documenting technical tradeoffs
- Expert-level document, spreadsheet, and slide craftsmanship, with excellent written communication
About this role
From the Ethos listing
About This Opportunity
We're working with a leading foundational AI lab to find experienced data engineers who can help train their latest language model on professional document, spreadsheet, and slide deck tasks.
We're looking for data engineers with 4+ years building software, AI/ML systems, or data infrastructure in a professional engineering or data role to create, evaluate, and refine AI-generated documents, spreadsheets, and slide decks across core workflows: technical design documents and architecture decision records, data pipeline and ETL documentation, model evaluation and AI system readiness reports, API specification and integration guides, data quality and validation test plans, and developer onboarding guides and system architecture diagrams.
Compensation: $80/hour
Commitment: Flexible, 5-20 hours per week (or more if desired)
Location: Fully remote, work on your own schedule
Start date: ASAP
Qualifications
- 4+ years as a software engineer, data engineer, or AI/ML practitioner writing technical design documents, data pipeline documentation, or API specifications
- Direct experience evaluating model outputs or system readiness and documenting technical tradeoffs for engineering or cross-functional stakeholders
- Expert-level document, spreadsheet, and slide craftsmanship, with excellent written communication and attention to detail
How long hiring takes
On the platforms we refer people to, half of hires came within about 25 days of applying. Time from applying to hire:
Prepare for the Ethos call
Ethos's AI interviewer asks about your Data Engineer background. These are our own practice questions for that work, not Ethos's.
How do you design an ETL pipeline to be idempotent so reruns don't duplicate data?
I design loads around upserts keyed on a natural or surrogate key rather than plain inserts, and I make each pipeline stage stateless with respect to its inputs so rerunning it with the same input produces the same output. Partitioning by a processing date and overwriting the whole partition on rerun is another reliable pattern, especially for batch pipelines.
When would you choose a star schema over a snowflake schema for a data warehouse?
A star schema keeps dimension tables denormalized, which trades storage and some data redundancy for simpler, faster queries. That's the right call when query performance and ease of use for analysts matter more than storage efficiency. A snowflake schema is worth the extra join complexity when dimension tables are large, change frequently, and normalization meaningfully reduces storage and update anomalies.
How do you handle schema evolution in a pipeline that ingests data from a source you don't control?
I treat the ingestion layer as schema-tolerant by landing raw data first, before any strict schema is enforced, and applying schema validation at a later transformation stage. This way a new or renamed field upstream doesn't break ingestion outright. I also version the schema explicitly so downstream consumers can detect and handle changes rather than silently breaking.
What's your approach to choosing between a batch and a streaming architecture for a new data pipeline?
The deciding factor is how fresh the data needs to be for the downstream use case, not how technically interesting streaming is. If consumers only need daily or hourly updates, batch is simpler to build, debug, and operate. Streaming is worth the added complexity only when there's a real requirement for near-real-time data, like fraud detection or live dashboards.
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Common questions
Do I need to keep applying for projects on Ethos?
No. When your profile is live, Ethos matches you to new projects from your career history, so companies find you. Each finished project raises your Expert Score, which brings you better-paid work.
How and when does Ethos pay experts?
Within 30 days of your invoice, after a project closes. Some AI training platforms pay weekly, so plan for the longer wait.
How many hours a week is this Data Engineer work?
Between 5 and 20 hours a week. At $80/hr that is $400 to $1,600 a week. You set your own hours.
What experience does this Data Engineer listing ask for?
The listing asks for 4+ years as a software engineer, data engineer, or AI/ML practitioner writing technical design documents, data pipeline documentation, or API specifications; direct experience evaluating model outputs or system readiness and documenting technical tradeoffs for engineering or cross-functional stakeholders; and expert-level document, spreadsheet, and slide craftsmanship, with excellent written communication and attention to detail. Ethos's interviewer builds your profile from your CV, so have the roles that show this ready to talk through.
What specific skills does this listing call for?
Coding is named directly in the listing. If you don't have hands-on experience with this, expect the screening to test for it.
How much does this specific role pay?
This listing is posted at $80/hr, an hourly rate. Pay can change after we check the listing, so confirm the rate on the platform's application page.
What happens when I click Apply on this listing?
The link opens Ethos's sign-up. There is no form for this listing: Ethos's AI interviewer asks about your career in a 5 to 15 minute call, builds your profile from it, and then matches you to projects like this one. Keep your CV open and cover the experience the Qualifications above ask for.
Who built Ethos and is it legitimate?
Yes. Ethos was founded in London in 2024 by James Lo (McKinsey, SoftBank Vision Fund) and Daniel Mankowitz (Google DeepMind research scientist and lead author of AlphaDev). It raised a $22.75M Series A led by a16z in May 2026, with General Catalyst and XTX Markets participating, and works with AI labs, private equity firms, hedge funds and consultancies. The link on this page is an affiliate link; it doesn't affect your pay or the projects you're offered.