Manager, Software Engineering, Data Platform

Role overview Lead a team responsible for the data platform that supports analytics, monitoring, compliance, and AI workloads. This hands-on engineering management role sets platform direction across ingestion, orchestration, warehousing, business intelligence, and real-time data services, while building reliable self-service capabilities and controlled access for people and AI agents.

Responsibilities - Own the platform vision, architecture roadmap, and priorities across data ingestion, pipelines, warehouse, BI, and real-time services. - Coach, hire, and develop senior engineers, helping individual contributors grow into technical leaders. - Establish data quality and reliability through service objectives, data contracts, lineage, automated checks, and clear ownership. - Develop governed AI and agent capabilities, including useful data context, natural-language access, operational agents, and auditable evaluations. - Enable teams to onboard sources, pipelines, dashboards, and monitoring without relying on ticket-based handoffs. - Stay close to technical work through design and code reviews, production debugging, incident response, and prototypes; manage service levels, costs, upgrades, and operational reporting.

Requirements - Six or more years in data, machine learning, or platform engineering, including at least four years managing software engineers. - Deep experience building and operating data platforms, including pipeline orchestration, warehouse architecture, governance, and production reliability. - A record of delivering complex platform work from planning through release, with strong judgment on technical tradeoffs. - Recent hands-on technical involvement, such as designing, prototyping, reviewing, or shipping production code; able to investigate difficult pipeline or warehouse issues. - Strong communication skills, including writing useful design documents and explaining systems to technical and business partners. - Understanding of model training and evaluation data, including dataset preparation, labeling, versioning, and risks such as leakage or drift.

Nice to have Experience with blockchain data, payments, fintech, or regulated systems; streaming and low-latency monitoring; data catalog, lineage, and access governance; cloud cost attribution; Python, Go, or Java services; agent frameworks or protocols; and leadership of a distributed team.