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Lead and grow a team of ~10 Forward Deployed Engineers to design, build, and productionize large-scale data and AI solutions on Databricks. Own delivery strategy, mentor engineers, act as senior technical escalation, shape engagements with customers and stakeholders, and develop reusable delivery patterns and best practices to drive measurable customer outcomes.

Serve as the field product manager for FDE engagements: scope C-suite value, define pilot MVP and PRD, prioritize engineering backlog, run agile delivery, manage launch and adoption, and hand off ongoing maintenance and growth opportunities.

Own and grow a marquee public sector account in Montreal by driving adoption and expansion of Databricks data and AI platform. Lead full sales cycle, build executive relationships, develop account strategy, identify AI/data use cases, orchestrate internal teams and partners, and exceed revenue targets.

Build and productionize customer-facing data and AI solutions on the Databricks platform. Own architecture and design decisions, develop end-to-end data pipelines and applications, integrate machine learning models, and guide enterprise customers through implementation and adoption. Collaborate with engineering, product, support, project managers, and customer stakeholders while delivering projects, documenting solutions, resolving issues, and creating reusable frameworks. Travel to customers approximately 20% of the time.

Lead Databricks’ Learning initiative across self-paced and enterprise-managed learning products. Define technical and product strategy, own roadmap and delivery from 0-to-1 through large-scale adoption, establish engineering excellence, and partner cross-functionally with R&D, Learning, Marketing, and Field Engineering. Manage approximately 12 engineers while scaling the organization and building AI-powered learning systems, interactive labs, identity, sandboxing, provisioning, and distributed infrastructure for millions of learners.

Build and productionize customer-facing data and AI solutions on the Databricks platform. Own architecture and design decisions while implementing end-to-end systems involving data engineering, pipelines, distributed computing, ML/AI models, and applications. Partner with customers, project managers, architects, engineering, and support teams to deliver measurable outcomes, guide adoption, resolve issues, and create reusable technical assets. The role requires customer immersion, enterprise stakeholder management, technical project delivery, and 20% customer travel.

Build and productionize customer-facing data, AI, and application solutions on the Databricks platform. Own architecture and design decisions, develop production-grade systems, integrate data pipelines with ML/AI models and user interfaces, and guide enterprise customers through implementation and adoption. Collaborate with engineering, product, support, project managers, and customer stakeholders while delivering measurable outcomes, reusable assets, documentation, training, and technical guidance. Travel to customers approximately 20% of the time.

Build and productionize customer-facing data and AI solutions on the Databricks platform. Own architecture and design decisions, develop end-to-end data pipelines and applications, integrate ML/AI models, and guide deployment. Partner with customers, engineering, product, project managers, and support teams to deliver measurable outcomes. Train customers, resolve implementation issues, contribute reusable frameworks, and influence the product roadmap. The role requires enterprise stakeholder management, technical project delivery, and 20% customer travel.

Build and productionize customer-facing data and AI solutions on Databricks. Own architecture and design decisions while delivering scalable data pipelines, applications, ML/AI integrations, and user interfaces. Lead technical project delivery, advise enterprise stakeholders, train customers, resolve implementation issues, and contribute reusable engineering assets and product feedback. The role requires strong cloud, distributed computing, software engineering, MLOps, documentation, and customer engagement skills, with travel to customers approximately 20% of the time.

Design, implement, and optimize low-level application frameworks and core system infrastructure. Focus on JVM internals, memory management, concurrency, and performance tuning. Collaborate on architecture, mentor junior engineers, troubleshoot complex system issues, and contribute to scalable distributed systems.