Senior Manager Data Engineering

Build and lead the team: Recruit, coach, and develop data and AI engineers. Establish clear ownership, development plans, and accountability for engineering quality and delivery. Deliver enterprise data products: Lead the development of scalable ingestion and transformation pipelines, curated datasets, and reusable data products across business domains. Develop AI-ready data capabilities: Guide semantic modeling, metadata pipelines, knowledge graph integrations, and enterprise context services that provide AI applications with consistent business definitions and relationships. Own production readiness: Establish service expectations, monitoring, incident response, support handovers, and cost controls for data pipelines and AI services. Manage delivery and partnerships: Own roadmaps, capacity planning, technical dependencies, and delivery across multiple initiatives. Demonstrate business value: Track data reliability, delivery performance, AI answer quality, adoption, time saved, and operating costs to guide investment decisions. Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience. Typically 10+ years of experience in data engineering, software engineering, or related disciplines, including 3+ years leading Data engineering teams. Proven experience delivering and supporting enterprise data platforms and business-critical data pipelines. Strong technical foundation in SQL, Python, dimensional modeling, distributed processing, and cloud architecture. Experience with Google Cloud services such as BigQuery, Cloud Storage, Composer/Airflow, and Dataform, or comparable cloud technologies. Practical experience delivering AI-enabled applications, including LLM integration, retrieval-augmented generation, tool calling, and evaluation. Understanding of semantic models, metadata management, knowledge graphs, and their role in grounding AI responses. Experience implementing automated testing, CI/CD, data quality monitoring, and production support practices. Demonstrated ability to manage competing priorities, lead cross-functional delivery, and communicate technical decisions clearly.