Data Engineer AI/ML

This role is hybrid with M/W/F on site at location on req

Schedule: M-F (8-5 with 1 hour non-billable lunch) with flexibility depending on business needs

Job Description

• This candidate is responsible for designing and developing in-house Security Information and Event Management (SIEM), Security Orchestration, Automation, and Response (SOAR), and investigative tools, with a primary focus on building robust, scalable, and automated security solutions.
• In this role, you will be responsible for the end-to-end design, development, testing, deployment, and maintenance of key components within our large-scale data infrastructure.
• This infrastructure is essential for advancing automation, enabling data-driven insights, and enhancing enterprise-wide security measures.

Responsibilities

• Develop and deploy scalable, production-ready software to drive automation and enable data-driven decision-making within the Enterprise Cybersecurity team, prioritizing resilience, performance, and security.
• Provide engineering support for the Enterprise Cybersecurity Operations team, with a focus on creating automated solutions that reduce manual intervention and operational overhead.
• Design and build event-driven, scalable systems to deliver timely alerts and automations, enhancing responsiveness and support for both the Cybersecurity team and external stakeholders.
• Integrate new data sources into our data lake to enhance visibility and extend security coverage across the environment, focusing on building reusable, automated data ingestion processes.
• Provide development and migration support for the integration of new tools and technologies, ensuring seamless onboarding within the Enterprise Cybersecurity environment.

Qualifications

• Extensive programming experience in object-oriented languages (e.g., Python, Go, Java) and SQL, with a proven track record in designing maintainable, scalable, and efficient solutions.
• Must have AI/ML experience
• Robust expertise in the following areas: distributed data processing, data engineering for high-volume data services, or developing scalable data streaming platforms for real-time analytics.
• Advanced proficiency in cloud and data infrastructure technologies (e.g., AWS, Databricks, Terraform, Apache Spark, Docker) with deep knowledge of development best practices, CI/CD pipelines, and cloud-native deployment.
• Comprehensive knowledge of RESTful APIs and data integration techniques to enable efficient, secure, and scalable data flow and communication between security systems and user-facing platforms.
• Strong familiarity with infrastructure-as-code tools such as Terraform or Ansible to automate and standardize security configurations across diverse environments.
• Hands-on experience with CI/CD pipelines, version control systems (e.g., Git), and modern software development practices to ensure high standards of consistency, quality, and automation in deploying and updating security tools.