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Mercor

Automates candidate screening and matching

Analytics Engineer

Data EngineeringUpdated on Nov 11
Full-timeSan Francisco, CA, USA$250k - $400k /yr

About the Job

Requirements

  • At least 8 years of experience in Analytics Engineering, Data Engineering, Data Science, or a related technical role.
  • Strong technical fundamentals in SQL, data modeling, Python, dbt, cloud data warehouses, and business intelligence tools such as Hex or Looker.
  • Ability to independently identify problems, define roadmaps, and deliver high-impact projects with minimal direction.
  • Ability to work directly with senior stakeholders, understand business priorities, and translate ambiguity into actionable solutions.
  • Ability to quickly master unfamiliar domains and technologies and leverage AI tools to increase productivity.
  • Track record of delivering high-quality work at speed, taking ownership beyond formal responsibilities, and working effectively in a demanding, fast-moving environment.

Responsibilities

  • Partner directly with stakeholders, identify high-impact problems, prioritize solutions independently, and execute a self-directed roadmap.
  • Develop reliable data pipelines, models, and metrics using SQL, dbt, and Snowflake.
  • Translate ambiguous business problems into production-ready data models, dashboards, and analytical tools.
  • Identify operational bottlenecks and build automations to eliminate manual work and improve efficiency.
  • Use AI coding agents and emerging tools to accelerate development and improve analytics engineering processes.

About the Company

Mercor

Automates candidate screening and matching

Mercor provides an AI-driven recruitment platform that automates key parts of hiring for both job seekers and employers. Job seekers upload their CVs and complete a 20-minute AI-led video interview, after which a proprietary language model generates detailed candidate profiles; employers receive automated resume screening, candidate matching, and initial interviews, with tailored AI interviews for specialized roles. The system uses a company-trained model to rank and recommend candidates by qualifications and predicted performance, and operates on a finder’s-fee revenue model with large-scale usage across software, operations, design, and other fields, including AI labs. Its goal is to speed up hiring while reducing bias and delivering better job-to-citizen matches.

Company size
201-500
Company stage
Series D
Total funding
$1.2B
Headquarters
Menlo Park, CA
Founded
2023

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