Director of Engineering

About the role

Vectra AI is looking for a leader to oversee the Data Platform organization, which manages the ingestion and analysis of massive volumes of security telemetry. You will manage the systems that power our detection engines, machine learning models, and autonomous security features. This role requires a balance of organizational management and deep technical architectural oversight.

Key facts

What you'll do

• Mentor and develop engineering managers and senior technical staff across global teams.
• Align platform strategy with Product, AI, Security Research, and Infrastructure departments.
• Oversee the delivery of large-scale platform projects while maintaining high operational standards.
• Define engineering practices for data quality, schema management, and service-level objectives.
• Collaborate with architects to pressure-test designs and ensure infrastructure is ready for AI workloads.
• Promote self-service tooling and APIs to support Data Science and product development.
• Manage production operations, including incident response and continuous improvement.

Requirements

• Minimum 10 years of software engineering experience.
• At least 5 years of experience leading engineering organizations.
• Demonstrated success in building and maintaining production-grade, large-scale data platforms.
• Technical proficiency in distributed systems, stream and batch processing, and high-scale storage including object stores, time-series systems, and columnar databases.
• Experience with Databricks and Spark-based processing.
• Experience managing production ML or AI infrastructure, such as feature stores or inference platforms.
• Experience with platforms supporting LLM applications, including embeddings, retrieval, and model serving.
• Strong understanding of SRE principles and operational reliability.

Nice to have

• Experience in cybersecurity, XDR, SIEM, NDR, or observability domains.
• Proven ability to optimize cloud performance and costs on AWS, Azure, or GCP.
• Background in streaming analytics or real-time detection systems.

Skills & tools

• Databricks
• Spark
• Distributed systems
• Large-scale data ingestion
• Machine Learning infrastructure
• LLM application support
• Cloud platforms (AWS, Azure, GCP)

Practical notes

This role requires the ability to communicate complex technical trade-offs to executive leadership while maintaining a hands-on approach to architectural decision-making.