Data Engineer
About the role
About Mercor Mercor's mission is to organize human intelligence to power the AI economy. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development. Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $2 million a day. 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.