Data Engineer

This job was selected by the SpringCube team to help AI, Data and Cloud Engineers discover relevant San Francisco Bay Area employers. Sign up to view the full employer details and apply directly with the hiring company.

Company Overview

A rapidly growing AI data company is building infrastructure that connects human expertise with frontier artificial intelligence models. Its platform enables millions of domain experts to contribute their knowledge to AI development, while its benchmarking and enterprise solutions help organizations understand and capture how professional expertise can be translated into AI-powered systems.

The organization operates at the intersection of artificial intelligence, data, research, and software engineering. Its teams work closely with researchers, operators, engineers, and AI companies to develop systems that support the advancement and evaluation of frontier AI technologies.

The organization is seeking a Data Engineer to bring a full-stack perspective to data engineering and build the pipelines that support Data Science, Engineering, Product, and other business functions.

The successful candidate will focus on data reliability, availability, and timeliness while working closely with Data Science and partner teams. This role will involve building scalable data infrastructure, improving data quality, designing production-ready data models, and ensuring reliable access to critical organizational data.

Key Responsibilities

• Build robust pipelines to ingest, transform, and consolidate data from diverse sources, including MongoDB, Airtable, PostHog, and production databases.
• Design and maintain dbt models and transformations that standardize disparate tables into clean, production-ready schemas.
• Implement scalable and fault-tolerant data workflows using Fivetran, dbt, SQL, and Python.
• Partner with engineers, data scientists, product teams, and business stakeholders to ensure data availability, accuracy, and usability.
• Own data quality and reliability across the data stack, from ingestion through consumption.
• Continuously improve pipeline performance, monitoring, scalability, and operational reliability.
• Develop data infrastructure that supports Data Science, Engineering, Product, and broader organizational requirements.
• Collaborate cross-functionally to identify data needs and develop reliable solutions.
• Establish and maintain data engineering best practices for modeling, transformation, monitoring, and quality assurance.
• Support the development of scalable data systems that can adapt to rapidly evolving business and AI requirements.

Required Qualifications

• Proven experience in data engineering.
• Strong proficiency in SQL and Python.
• Experience with modern data stack technologies such as Fivetran, dbt, and Snowflake or similar platforms.
• Experience building and maintaining large-scale ETL/ELT pipelines.
• Experience integrating data from heterogeneous sources, including databases, analytics platforms, and SaaS applications.
• Strong understanding of data modeling and schema design.
• Knowledge of data transformation best practices.
• Familiarity with data governance, monitoring, and data quality assurance.
• Ability to work effectively with engineering, product, operations, and data science teams.
• Strong problem-solving and communication skills.
• Ability to operate effectively in a fast-paced, highly collaborative environment.

Preferred Qualifications

• Experience supporting machine learning workflows.
• Experience supporting analytics platforms and data-driven products.
• Experience working with production-scale data infrastructure.
• Familiarity with reliability and monitoring practices for modern data pipelines.

Work Environment

• Full-time, on-site position in San Francisco.
• The organization operates in-person five days a week.
• Opportunities to work on infrastructure and research-adjacent projects involving frontier AI technologies.
• Collaboration with engineering, research, operations, and AI-focused teams.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

1.No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.**