Senior Data Engineer - Product Security
Red HatAbout the role
About the Job
At Red Hat, trust is our product. As we scale our portfolio across the open hybrid cloud, the volume and complexity of our security data are exploding. We are looking for a Senior Data Engineer to bring order to this chaos and turn raw telemetry into strategic advantage.
The Red Hat Product Security team ensures that our software is secure by design and compliant by default. In this role, you will be the technical lead for our data infrastructure. You will architect the systems that ingest, correlate, and analyze security signals from millions of lines of code and thousands of upstream communities.
You will be building the "single pane of glass" that drives Red Hat’s risk decisions. You will have the autonomy to choose the right tools for the job and the mandate to integrate Artificial Intelligence (AI) into our core workflows, automating the heavy lifting of vulnerability analysis so our humans can focus on what matters.
What you will do
Architect Scalable Data Ecosystems: Design and oversee the implementation of high-performance data architectures (Data Lake/Mesh) capable of handling heterogeneous security data at enterprise scale.
Drive AI & ML Strategy: Lead the technical integration of AI models into our data pipelines. You will determine how we use LLMs and predictive modeling to automate vulnerability classification and anomaly detection.
Lead Data Governance: Establish the "source of truth." Define data models, schemas, and quality standards that ensure our security metrics are audit-proof and reliable for executive decision-making.
Optimize for Speed and Cost: Analyze the performance of our data processing (ETL/ELT) and storage systems, identifying bottlenecks and re-architecting legacy flows for efficiency.
Mentorship and Technical Leadership: Act as a force multiplier. Mentor junior engineers, conduct code reviews, and foster a culture of engineering excellence and psychological safety within the team.
Stakeholder Partnership: Translate complex security and business requirements into technical roadmaps. You will work directly with Security Architects and Product Managers to define what is possible.
What you will bring
We are looking for a builder with a history of solving hard problems. While specific tool knowledge is useful, your understanding of data engineering principles and your ability to adapt are paramount.
Senior-Level Experience: Typically, 5+ years of experience in data engineering, backend software development, or site reliability engineering. You have seen systems fail at scale and know how to design them to survive.
Architectural Fluency: Deep understanding of distributed data systems and modeling techniques. You know when to use a relational database, when to use a graph database, and how to design a Star schema that actually works.
Database Expertise: Deep experience in performance tuning and optimization, along with advanced database troubleshooting skills to identify bottlenecks and ensure system reliability at scale.
AI/ML Integration: Practical experience integrating AI/ML workflows into data systems is a strong differentiator. We want someone who understands the data engineering requirements to support and operationalize AI.
Advanced Coding Skills: Proficiency in Python, SQL, and experience with data orchestration tools (e.g., Airflow, Dagster) and containerization (Kubernetes/OpenShift).
Strategic Communication: You can debate technical trade-offs with engineers and explain the impact of those trade-offs to non-technical leadership.
The "Generalist" Mindset: You are not dogmatic about tools. You use the right tool to solve the problem, and you are willing to dive into legacy code to modernize it.
Pay Transparency
Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat’s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary ran
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