Data Engineer
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- Work arrangement
- onsite
- Employment
- FullTime
- Stated compensation
- USD 160,000–500,000 1 YEAR
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Requirements
1 with evidence8 unclear or unstated- Remote policy
- onsite
“Provider workplace type: onsite”
From employer posting metadata - Employment type
- FullTime
- Compensation
- USD 160,000–500,000 1 YEAR
8 fields not stated in this posting
- Employer sponsorship
- Not specified
- Geographic scope
- Not specified
- Degree requirement
- Not specified
- Security clearance
- Not mentioned
- Timezone / hours
- Not specified
- Citizenship requirement
- Not mentioned
- Minimum experience (years)
- Not specified
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Original job description
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Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.
Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
About the Role
We’re looking for someone who wants to bring a full-stack perspective to data. As a Software Engineer supporting our Data function, you will be responsible for creating and maintaining pipelines that enable our Data Science, Engineering, and Product teams, and the wider Mercor organization.
Your focus will be on data reliability, availability, and timeliness, with a focus on collaboration (and significant operational crossover with) our Data Science team and the many partner functions.
What You’ll Work On
Building robust pipelines to ingest, transform, and consolidate data from diverse sources (e.g., MongoDB, Airtable, PostHog, production databases).
Designing dbt models and transformations to standardize and unify many disparate tables into clean, production-ready schemas.
Implementing scalable, fault-tolerant data workflows with Fivetran, dbt, SQL, and Python.
Partnering with engineers, data scientists, and business stakeholders to ensure data availability, accuracy, and usability.
Owning data quality and reliability across the stack, from ingestion through to consumption.
Continuously improving pipeline performance, monitoring, and scalability.
What We’re Looking For
Proven experience in data engineering, with strong knowledge of SQL, Python, and modern data stack tools (Fivetran, dbt, Snowflake or similar).
Experience building and maintaining large-scale ETL/ELT pipelines across heterogeneous sources (databases, analytics platforms, SaaS tools).
Strong understanding of data modeling, schema design, and transformation best practices.
Familiarity with data governance, monitoring, and quality assurance.
Comfort working cross-functionally with engineering, product, and operations teams.
Bonus: prior experience supporting machine learning workflows or analytics platforms.
Why Mercor
Impact: Your work powers how the world’s leading AI labs train and test their models.
Learning: Get early insights into frontier model capabilities months before the market.
Growth: Work on both infrastructure and research-adjacent projects with fast paths to ownership.
Benefits
Bi-annual performance bonus structure
Generous equity grant vested over 4 years
Up to $15k Relocation bonus
$10K housing bonus (if you live within 0.5 miles of our office)
$1.5K monthly stipend for meals
Free Equinox membership
$200 monthly laundry reimbursement
$200 monthly personal wellness reimbursement
Health, Dental, Vision insurance
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