Data Engineer

Mercor mercor.com Open · verified Oct 24, 2026

On-site San Francisco Full-time Mid-level Data Engineering

At a glance

Mid-level Data Engineering role at Mercor. San Francisco · full-time · $160,000–$500,000 base.

SalaryStated by Mercor

$160,000 – $500,000

Base salary for this role, in USD per year, as published in the posting.

This role, from MercorOur estimate for the market: $181,250–$299,375Median $211,250

Worked out from 22 postings that stated pay, median $211,250. Last updated Oct 13, 2026.

RoleData Engineering

SeniorityMid-level

LocationSan Francisco

WorkplaceOn-site

EmploymentFull-time

Base pay$160,000–$500,000

PostedMay 7, 2026 · 5mo ago

Role brief

dataroles summary, based on the employer's posting.

What you'll do

Build pipelines that bring together information from MongoDB, Airtable, PostHog, and production databases
Use dbt models to turn scattered tables into consistent schemas ready for production
Create resilient data workflows with Fivetran, dbt, SQL, and Python
Work with engineering, data science, product, and business teams to keep data usable and dependable
Track data quality across the pipeline and improve its speed, monitoring, and ability to scale

What you bring

Hands-on data engineering experience, including strong SQL and Python skills and familiarity with modern data tools
Experience maintaining large ETL or ELT pipelines that draw from databases, analytics platforms, and SaaS products
A solid grasp of data models, schema design, and transformation practices

Who this fits

This role suits a data engineer comfortable owning reliability from ingestion through use and working closely with data science and partner teams. You'll work in person five days a week at the San Francisco, NYC, or London office.

From the employer

About Mercor

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