AI-driven threats and human error are today’s biggest enterprise security risks. Cybercriminals don’t hack systems—they exploit people. Human errors drive 90% of security breaches, making human behavior the primary and growing attack surface.
Fable is a security platform that mitigates human risks to the business. We built the first AI-native Human Behavior Platform to eliminate human risk wherever employees work. By leveraging GenAI and behavioral systems, we automatically detect at-risk employees, deliver personalized interventions at critical moments, and remove tangible risks to the business — at scale.
Backed by Greylock Partners and founded by early Abnormal Security team members, Fable is solving cybersecurity’s biggest challenge in a multi-billion-dollar market. Our team includes alumni from Meta, Twitter, Flexport, and top-tier universities like Waterloo, Columbia, Berkeley, Purdue, CMU, Stanford, USC, and UW. We are experiencing explosive growth, making this a career-defining opportunity to join and shape the future of security.
Build and scale the foundational data systems behind a new category in cybersecurity.
Partner closely with data scientists, backend engineers, and security practitioners to operationalize data at scale.
Work on unique challenges in data product versioning, historical tracking, and scalable data releases across dynamic human risk behaviors.
Join a small, high-caliber team poised to double in size within the next year.
As a Data Engineer at Fable, you will own the architecture, development, and reliability of our data pipelines and warehouse. You’ll build scalable, observable, and versioned data infrastructure that powers Fable’s Human Behavior Platform — from raw ingestion to analytical insights.
You will play a central role in ensuring that Fable’s data foundation can support real-time behavioral analysis, historical tracking, and large-scale experimentation across diverse data sources.
Design, build, and maintain scalable data pipelines in Databricks , leveraging dbt , SQL , and Python for transformation and orchestration.
Manage data ingestion and versioning across multiple behavioral and security data sources.
Implement data versioning frameworks and reproducible datasets for experimentation and risk modeling.
Build tooling to support scalable data releases and consistent historical tracking of employee risk behavior.
Partner with data scientists to productionize analytical and ML-ready datasets.
Define and maintain best practices for data quality, documentation, and lineage.
Optimize performance, storage, and cost efficiency across the data platform.
Contribute to infrastructure that enables secure and compliant data workflows in AWS (S3, IAM, Lambda, etc.) .
3–7 years of experience as a Data Engineer, Analytics Engineer, or similar role.
Proficiency in SQL , Python , and dbt for data modeling and transformation.
Experience building and maintaining pipelines in Databricks , Spark, or similar distributed compute environments.
Strong understanding of data versioning , reproducibility , and ETL orchestration .
Hands-on experience with AWS S3 and modern data storage patterns.
Ability to design for scalability, reliability, and observability.
Experience collaborating closely with data scientists, designers, and backend engineers in a fast-moving environment.
Experience with data release management , feature stores , semantic layers, and/or historical record tracking for behavioral or security data.
Familiarity with ML Ops , Airflow , or other orchestration tools.
Experience in a cybersecurity, risk, or compliance-focused domain.
Prior experience in an early-stage startup or high-growth company.
We work from our San Francisco office three days a week , fostering deep collaboration and creative problem-solving.
We value impact over process - every engineer directly shapes how our platform detects and mitigates human risk.
We emphasize mutual trust, technical rigor, and building differentiated, AI-driven security products.
The estimated salary range for this position is $160,000 – $220,000/year . Total compensation may also include Restricted Stock Units (RSUs). Final compensation will depend on experience, qualifications, and other factors.
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