Software Engineer - Data Infrastructure

Fable Security•San Francisco, CA

3d•$160,000 - $225,000

About The Position

AI-driven threats and human error are today’s biggest enterprise security risks. Cybercriminals don’t hack systems—they exploit people. Human errors drive 70% of security breaches, making human behavior the primary and growing attack surface. At Fable, we believe that the right tools can convert people from targets to another layer of defense. Fable is the human risk platform that directly shapes employee behavior. Designed for simplicity and enterprise scale, our agentic platform synthesizes complex employee data, pinpoints risky behaviors, and deploys highly relevant interventions to people automatically, in real time, right where they work. Backed by Redpoint Ventures and 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, UCLA, and USC. We are experiencing explosive growth, making this a career-defining opportunity to join and shape the future of security. Why Join Us Build and scale the foundational data infrastructure powering a category-defining product Work closely with engineering, data science, and product teams to operationalize data at scale Join a small, high-caliber team where you’ll have outsized ownership and impact At an early-stage company, every engineer directly shapes how the product works and evolves. You’ll help define not just the system—but the way the company thinks about data. Your Role As a Data Infrastructure Engineer, you will own the architecture, scalability, and reliability of our data platform. You’ll design and build the systems that power everything from real-time product features to internal analytics and machine learning workflows—from raw ingestion to production-ready datasets. You’ll develop the architecture and establish best practices on which our data-driven products are built. This is a highly cross-functional role where you’ll partner closely with engineering, data, and product teams to ensure our data foundation scales with the company.

Requirements

3–7+ years of experience in data infrastructure, infrastructure, or platform engineering
Strong understanding of real-time and batch data processing systems, including stream processing, messaging architectures, and large-scale data workflows (Spark, Kafka, etc.)
Experience with modern data tooling (dbt, Airflow, etc.)
Strong understanding of data modeling, ETL/ELT, reverse ETL, and orchestration
Experience designing for scalability, reliability, and observability
Hands-on experience with cloud platforms (AWS/GCP) and storage systems
Ability to operate in a fast-moving startup environment with high ownership

Nice To Haves

Familiarity with ML infrastructure or feature stores
Experience with data versioning, lineage, or reproducibility systems
Background in security, compliance, or high-sensitivity data domains
Previous experience at an early-stage startup

Responsibilities

Design, build, and maintain scalable data pipelines and infrastructure
Develop batch and streaming systems for ingesting and processing large-scale data
Own core components of the data platform (warehouse, lake, orchestration, tooling)
Implement data modeling, transformation, and versioning frameworks
Ensure data quality, observability, and reliability across pipelines
Optimize systems for performance, cost, and scalability
Build tooling to support reproducible datasets and experimentation
Partner with data scientists and engineers to productionize ML and analytics workflows
Contribute to secure and compliant data infrastructure in cloud environments