Job Responsibilities: Build, grow, and mentor high-performing engineering teams across multiple geographies Develop engineering managers and senior technical leaders through coaching, sponsorship, and career development Foster an inclusive culture grounded in trust, ownership, and accountability Create conditions for strong execution with clear priorities, healthy team rhythms, and focus on meaningful work Align priorities and investments 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 anticipate evolving data requirements and scaling challenges Influence company-wide direction on data, AI, and platform capabilities Lead delivery of large-scale platform initiatives, balancing innovation, reliability, and speed Define and enforce 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 maintaining focus on customer impact and long-term platform health Engage deeply on architecture for scalable, reliable, AI-ready infrastructure and pressure-test designs with platform architects Champion a platform-first mindset with self-service APIs, robust developer tooling, observability, and clear documentation Drive adoption of modern capabilities including feature stores, vector storage, and model serving infrastructure Champion the use of AI to improve engineering productivity, software delivery, and incident response Job Requirements: 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 in large-scale data infrastructure: distributed systems, ingestion pipelines, stream and batch processing, and storage at scale (object stores, columnar databases, time-series systems) Hands-on experience with Databricks, Spark-based processing, and the Databricks platform ecosystem Experience running ML or AI workloads in production, including platforms, feature stores, and inference infrastructure Operational mindset with strong grounding in SRE principles, incident response, and reliability practices Experience building or operating platforms that support LLM-based applications, including retrieval, embeddings, and model serving Demonstrated ability to champion and apply AI to improve engineering productivity, software delivery, and incident response Preferred: Background in cybersecurity, NDR, SIEM, XDR, observability, or related domains Preferred: Proven ability to optimize cloud costs and performance at scale on AWS, Azure, or GCP Preferred: Experience with real-time detection systems or streaming analytics. This role owns the ingestion, processing, storage, and analysis of petabytes of security telemetry-spanning network packets, logs, identity signals, and security events-and advances the platform to power modern AI workloads, autonomous security workflows, and next-generation analytics.