Senior AI-First Data Engineer

Role overview Join a high-growth technology organization building an AI-native data platform that powers analytics, automation, and intelligent agents across the business. This senior role focuses on evolving the data architecture from legacy batch systems to a cloud-native, streaming-first foundation that serves both human and machine consumers. The position sits at the intersection of data engineering, AI enablement, and platform transformation.

Responsibilities - Lead the evolution of a cloud-native, AI-first data platform, migrating pipelines from batch to near-real-time and streaming architectures - Build data products consumable by AI agents, analytics systems, and business users, including semantic layers and metadata management - Improve reliability, observability, governance, and performance across the data stack while establishing engineering standards - Use AI to accelerate development, testing, documentation, monitoring, and operational workflows - Enable AI agents to safely query, understand, and act on business data through well-designed interfaces and guardrails - Mentor engineers, conduct architecture reviews, and raise the technical bar across the data and analytics organization

Requirements - Senior-level experience designing and operating cloud-native data platforms at scale - Strong hands-on skills with Databricks, dbt, Airflow, Python, and SQL - Background building AI-ready data assets, semantic layers, and metadata-driven architectures - Familiarity with RAG systems, vector databases, knowledge graphs, or enabling AI agents to consume operational data - Track record of mentoring engineers and leading architecture decisions across teams - Ability to partner effectively with Product, Engineering, GTM, and AI stakeholders

Nice to have - Experience transitioning from on-premise batch systems to streaming-first infrastructure - Background enabling self-service analytics for non-technical stakeholders without sacrificing governance

Benefits and work setup - Remote-friendly distributed team model with collaboration hubs in multiple global locations - Environment described as AI-native, with active use of AI tooling across engineering and operations