Staff Technical Program Manager
Role Overview
Staff Technical Program Manager in the Technical Program Management Organization, reporting to the Director of Technical Program Management.
Lead large-scale, cross-functional programs across the Data and AI Platform organization as a senior individual contributor.
Drive execution across data engineering, ML engineering, and applied AI, improving delivery predictability, managing dependencies, and influencing strategy.
Partner with Engineering, Product, Data Science, Architecture, and Operations leaders on initiatives supporting AI-powered products and internal data capabilities.
Key Skills for This Role
Full Job Posting
About Kaseya
Kaseya provides AI-powered IT management and cybersecurity software for managed service providers and internal IT organizations.
Its platform helps organizations manage, secure, and automate IT environments; the company serves customers in more than 20 countries and manages over 15 million endpoints.
Role overview
Staff Technical Program Manager in the Technical Program Management Organization, reporting to the Director of Technical Program Management.
Lead large-scale, cross-functional programs across the Data and AI Platform organization as a senior individual contributor.
Drive execution across data engineering, ML engineering, and applied AI, improving delivery predictability, managing dependencies, and influencing strategy.
Partner with Engineering, Product, Data Science, Architecture, and Operations leaders on initiatives supporting AI-powered products and internal data capabilities.
Strategic program leadership and execution
- Lead enterprise-scale data and AI platform programs across engineering and product organizations.
- Define execution plans, milestones, dependencies, and measurable outcomes; influence prioritization, sequencing, and delivery strategy.
- Coordinate Data Engineering, ML Engineering, Applied AI, Product, Security, and Infrastructure teams.
- Align stakeholders, establish governance and operating cadences, and partner with technical leaders on trade-offs, architecture, and delivery plans.
Risk and program visibility
- Identify risks involving model readiness, data quality, pipeline reliability, infrastructure, and organizational or technical dependencies; drive mitigation and issue resolution.
- Manage dependencies across data platforms, product engineering, and shared services; escalate with context and recommendations.
- Provide executive reporting and dashboards on milestones, risks, blockers, and program health.
- Improve delivery predictability and execution discipline and contribute to TPM practices for data and AI programs.
What success looks like within 12 months
Deliver large-scale data and AI platform initiatives predictably.
Improve visibility into risks, pipeline dependencies, model delivery milestones, and overall program health.
Strengthen alignment across data engineering, ML engineering, and product teams.
Manage risks proactively with minimal delivery surprises and build 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 leading complex technical programs in SaaS, cloud, or enterprise software environments.
- Experience managing programs involving data engineering, ML infrastructure, AI/ML product delivery, or data platform development.
- Ability to influence across organizations without direct authority; strong communication, analytical, and problem-solving skills.
- Ability to work effectively in fast-paced, ambiguous environments.
Preferred qualifications
- Hands-on familiarity with modern data-stack technologies such as Snowflake, dbt, Spark, Kafka, or Airflow, or ML/AI platforms such as SageMaker, Vertex AI, or MLflow.
- 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 methods in data and ML contexts.
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