Director of Engineering (Data Platform)

Director of Engineering Data Platform

• Build, grow, and mentor high-performing engineering teams across multiple geographies.

• Develop engineering managers and senior technical leaders through coaching, sponsorship, and career investment.

• Foster an inclusive culture built on trust, ownership, and accountability, where distributed teams can do their best work.

• Create the conditions for strong execution: clear priorities, healthy team rhythms, and an environment where engineers can focus on meaningful work.

• Align priorities and investment decisions with Product, Security Research, AI, Infrastructure, and Customer teams.

• Communicate platform strategy, progress, risks, and trade-offs clearly to executive leadership.

• Partner with customer-facing teams to understand evolving data requirements and scaling challenges.

• Influence company-wide direction around data, AI, and platform capabilities.

• Lead delivery of large-scale platform initiatives, balancing innovation, reliability, and speed.

• Set engineering best practices across data processing, schema evolution, data quality, and service-level objectives.

• Own production operations, incident management, and continuous improvement.

• Ensure predictable delivery while keeping a clear line of sight to customer impact and long-term platform health.

• Engage deeply on architecture decisions for scalable, reliable, and AI-ready infrastructure, able to ask hard questions, identify gaps, and pressure-test designs alongside platform architects.

• Champion a platform-first mindset with self-service APIs, strong developer tooling, observability, and documentation that accelerate Data Science, AI/ML, and product teams.

• Drive adoption of modern platform capabilities including feature stores, vector storage, and model serving infrastructure.

• Champion the use of AI to improve engineering productivity, software delivery, and incident response.

• 10+ years of software engineering experience, including 5+ years leading engineering organizations.

• Proven track record building and operating large-scale data platforms in production.

• Deep expertise across large-scale data infrastructure: distributed systems, data ingestion pipelines, stream and batch processing, and storage systems at scale including object stores, columnar databases, and time-series systems.

• Champion the use of AI to improve engineering productivity, software delivery, and incident response.

• Experience with Databricks, including Spark-based processing and the Databricks platform ecosystem.

• Experience running ML or AI workloads in production, including platforms, feature stores, or inference infrastructure.

• Operational mindset: SRE principles, incident response, and a culture of reliability.

• Background in cybersecurity, NDR, SIEM, XDR, observability, or related domains.

• Track record optimizing cloud costs and performance at scale on AWS, Azure, or GCP.

• Background inreal-time detection systems or streaming analytics

• A platform trusted and widely adopted by Data Science, AI, and engineering teams, known for reliability, self-service, and ease of use.

• Faster time-to-production for analytics, detections, and AI-powered applications.

• Reduced time from telemetry ingestion to model experimentation and deployment.

• High-performing teams with strong leadership pipelines and a culture people want to stay in.

• Industry-leading infrastructure supporting both advanced detections and autonomous security operations.