Staff Technical Program Manager

Staff Technical Program Manager 

Technical Program Management Organization

Kaseya, Vancouver

Reports To: Director, Technical Program Management

 

Role Overview

We are seeking an experienced Staff Technical Program Manager to lead large-scale, cross-functional programs across Kaseya's Data and AI Platform organization.

This is a senior individual contributor role focused on driving execution across data engineering, ML engineering, and applied AI teams — improving delivery predictability, managing complex technical dependencies, and influencing strategy at scale.

You will partner closely with Engineering, Product, Data Science, Architecture, and Operations leaders to drive alignment, surface risks early, and ensure high-quality delivery of initiatives that power Kaseya's AI-driven product experiences and internal data capabilities.

 

Key Responsibilities

Strategic Program Leadership

• Lead enterprise-scale data and AI platform programs spanning multiple engineering and product organizations
• Define execution plans, milestones, dependencies, and measurable outcomes.
• Influence prioritization, sequencing, and delivery strategy in partnership with Data and AI leadership.

Cross-Functional Execution

• Coordinate execution across Data Engineering, ML Engineering, Applied AI, Product, Security, and Infrastructure teams.
• Drive alignment across stakeholders with competing priorities and complex technical dependencies.
• Establish governance mechanisms, operating cadences, and execution frameworks suited to data and AI program rhythms.
• Partner with senior technical leaders on trade-offs, architectural decisions, and delivery plans.

Risk & Dependency Management

• Identify organizational, technical, and data-specific risks early — including model readiness, data quality, pipeline reliability, and infrastructure dependencies.
• Drive mitigation planning and resolution of complex cross-functional issues.
• Manage dependency networks across data platform, product engineering, and shared services.
• Escalate effectively with clear context and recommendations.

Program Visibility & Operational Excellence

• Provide executive-level reporting on milestones, risks, blockers, and overall program health.
• Build dashboards and reporting frameworks that give leadership clear visibility into data and AI initiative progress.
• Improve delivery predictability and execution discipline across data and ML workstreams.
• Contribute to TPM best practices and operating models for data and AI program management.

 

What Success Looks Like

Within 12 months:

• Successful delivery of large-scale data and AI platform initiatives with predictable execution.
• Improved visibility into risks, pipeline dependencies, model delivery milestones, and overall program health.
• Strong cross-team alignment between data engineering, ML engineering, and product teams.
• Proactive risk management with minimal delivery surprises.
• Trusted partnerships with Data, AI, Engineering, and executive stakeholders.

 

Basic Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field.
• 10+ years of experience leading complex technical programs in SaaS, cloud, or enterprise software environments.
• Experience managing programs that involve data engineering, ML infrastructure, AI/ML product delivery, or data platform development.
• Proven ability to influence across organizations without direct authority.
• Strong communication, analytical, and problem-solving skills.
• Ability to operate effectively in fast-paced, ambiguous environments.

Preferred Qualifications

• Hands-on familiarity with modern data stack technologies (e.g., Snowflake, dbt, Spark, Kafka, Airflow) or ML/AI platforms (e.g., SageMaker, Vertex AI, MLflow, or similar).
• Experience coordinating LLM integration, RAG pipelines, or AI feature delivery in a product context.
• Experience with platform engineering, cloud infrastructure, or data governance programs.
• Familiarity with Agile or hybrid delivery methodologies in data and ML contexts.