Software Engineer - Data Pipelines

Who is Tenable?

Tenable® is the Exposure Management company. Over 40,000 organizations around the globe rely on Tenable to understand and reduce cyber risk. Our global employees support 65 percent of the Fortune 500, 50 percent of the Global 2000, and large government agencies. Come be part of our journey!

What makes Tenable such a great place to work?

Ask a member of our team and they’ll answer, “Our people!” We work together to build and innovate best-in-class cybersecurity solutions for our customers; all while creating a culture of belonging, respect, and excellence where we can be our best selves. When you’re part of our #OneTenable team, you can expect to partner with some of the most talented and passionate people in the industry, and have the support and resources you need to do work that truly matters. We deliver results that exceed expectations and we win together!

Your Role

• You'll own the whole lifecycle of data pipelines running in 15+ regions, building them from start to finish.
• You'll write big, distributed processing jobs to handle massive datasets.
• You'll build and maintain pipelines that feed our product's access layer—think threat intelligence, assets, and detections.
• Quality is huge: you’ll build frameworks to check data health, run conditional checks, and keep everything observable
• You’ll implement distributed tracing across all your pipelines.
  
  

• We believe in owning your work end-to-end without annoying bottlenecks.
• We're AI-native, using custom plugins and protocols to make development smoother.
• We take engineering rigor seriously (think dead-code detection, constant security scans, and always-on production monitoring).
• Yes, we have on-call, but we’ve built tools to automate the pain out of it.
  
  

• 4+ years building production data pipelines at scale
• Solid Python 3.10+ chops (Pydantic, async, decorators).
• Apache Airflow - DAG authoring, MWAA or self-hosted production experience
• PySpark - job authoring, optimization, EMR or Databricks
• Snowflake expertise (Snowpark, schema design).
• AWS knowledge (S3, SQS/SNS, EMR).
• Data quality tooling: Great Expectations, Monte Carlo, or equivalent
• CI/CD with artifact packaging and staged deployments
• Strong testing discipline: pytest, moto, integration tests against real or mocked cloud services