Hybrid Data Engineer

Posted last month

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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

Job title

Data Engineer

Job type

Experience level

Mid levelSenior

Salary

$160,000 - $220,000 per year

Degree requirement

Bachelor's Degree

Location requirements

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