Sr. Manager leading data engineering team to optimize data infrastructure for insurance. Driving innovative data solutions and managing cross-functional collaborations within a remote setup.
Responsibilities
Manage a team of Data Engineers and Data Analytics Engineers owning our data infrastructure, including our medallion layers, data pipelines, and collaborating closely with MLEs on LLM Operations and agentic products.
Navigate ambiguity, driving innovation through rapid prototyping and iterative development in cross-functional teams
Partner with function leads and senior leadership to align growth paths, performance expectations, and strategic goals with technical excellence standards.
Translate the Data Engineering vision into clear objectives, priorities, and milestones, ensuring effective project scoping, resourcing, and delivery.
Define and refine agile workflows tailored to data engineering, managing key rituals such as design sessions, sprint planning, and retrospectives.
Serve as the primary delivery interface across Product, Engineering, and Customer Success, ensuring alignment, transparent communication, and continuous improvement of team operations.
Drive hiring excellence by attracting, developing, and scaling a high-performing team aligned with business needs.
Requirements
8+ years of experience in data science, data engineering, or machine learning–related roles.
4+ years in a people management or delivery management role (directly leading technical contributors).
Proven experience managing cross-functional ML technical teams and driving delivery across multiple workstreams.
Strong understanding of data science lifecycle (from ideation and experimentation to deployment and maintenance).
Practical experience with agile methodologies (Scrum, Kanban) and delivery facilitation.
Excellent communication, organizational, and stakeholder management skills.
Ability to balance technical depth with delivery discipline, ensuring outcomes, not just output.
Experience with LLM Ops, agentic framework, data governance, and productionization best practices.
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