Data Engineer
Fable
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • 🇺🇸 United States
Visit company websiteSalary
💰 $160,000 - $220,000 per year
Job Level
Mid-LevelSenior
Tech Stack
AWSETLPythonSparkSQL
About the role
- 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.).
Requirements
- 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.
Benefits
- Health insurance
- Stock options
- Paid time off
- Flexible work arrangements
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
SQLPythondbtDatabricksSparkdata versioningETL orchestrationdata modelingdata transformationdata pipelines
Soft skills
collaborationdesign for scalabilityreliabilityobservability