Senior Software Engineer, Data Platform & AI Enablement

About the role

The Knowledge Platform team is at the heart of our company's data and AI strategy. We are building the foundational infrastructure that empowers the entire company to leverage data, AI, and ML into business impact. Our mission is to evolve our full data ecosystem—encompassing both platform and models—into a fully AI agent-ready infrastructure. We will empower customers to engage directly with the data platform to extract actionable value through capabilities like analytics and natural language querying, while also upgrading the platform to deliver robust, real-time performance for instant, data-driven decision-making.

Responsibilities

• Lead the architecture and construction of core infrastructure components, taking full ownership of technical decision-making and custom development from foundational storage to high-availability service layers.
• Perform deep-dives into systems internals and performance tuning—focusing on state management, checkpointing, and exactly-once semantics—to ensure millisecond-level accuracy and reliability across 30+ global regions.
• Develop the "Knowledge Platform" to power AI-driven products through scalable vector indexing, agentic workflows, and real-time data streaming.
• Own the full SDLC for high-performance distributed systems that process petabytes of data at a global scale.
• Serve as a technical beacon and mentor for mid-level engineers, driving high-quality project delivery through meticulous design reviews and hands-on technical leadership.

Requirements

• 5+ years of experience building and operating large-scale distributed systems or infrastructure platforms.
• A deep understanding of computer science fundamentals, including distributed systems, memory management, and networking protocols.
• Proficiency in Java, Kotlin, or Go, with hands-on experience (or the desire to deep-dive) into the internals of Kafka, Flink, Spark, Kubernetes, or OLAP engines.
• A proven track record of taking 0-1 ownership of complex technical challenges, from initial design to production stability.
• Intrinsically motivated to explore emerging tech in AI/ML infrastructure and real-time systems to create tangible business impact.

Preferred Qualifications

• Hands-on design experience in crafting data processing patterns for a modern Lakehouse architecture.
• Contribute to the design and development of standard framework modules, high-performance services, and client libraries for big data using tools like GCP, Databricks, BigQuery, DataProc, Kafka, Kubernetes, Spark, DataFlow, Google Cloud Storage, and Airflow.
• Excellent written and verbal communication skills tailored for diverse audiences (leadership, users, company-wide).
• Rapidly evaluate various technologies and conduct proof of concepts to drive architecture design.
• Experience thriving in a complex environment.

Who You Are

Experience: Have 5+ years of experience building and operating large-scale distributed systems or infrastructure platforms. First Principles Thinking: Possess a deep understanding of computer science fundamentals, including distributed systems, memory management, and networking protocols. The "Builder" Stack: Are proficient in Java, Kotlin, or Go. You should have hands-on experience (or the desire to deep-dive) into the internals of Kafka, Flink, Spark, Kubernetes, or OLAP engines. Ownership Mindset: Have a proven track record of taking 0-1 ownership of complex technical challenges, from initial design to production stability. Curiosity & Impact: Are intrinsically motivated to explore emerging tech in AI/ML infrastructure and real-time systems to create tangible business impact.