Responsibilities
- Design and sustain comprehensive data and AI technology strategies across the full lifecycle
- Guide engineers and technical leads with proven design patterns and direct support on intricate implementations
- Lead architectural decisions using documented ADRs, established standards, and design evaluations
- Conduct architectural and code reviews while analyzing system performance to uphold quality and delivery pace
- Promote teamwork, shared learning, and ongoing development via coaching and collaborative sessions
- Enable controlled access to data through defined contracts, organized datasets, and reliable serving methods
- Create consistent data pipeline patterns for ingestion, transformation, storage, delivery, and retirement
- Define platform-wide norms for workflow orchestration, reliability, SLAs, retries, idempotency, and monitoring tools
- Implement enterprise-wide data quality frameworks covering key data points, validation rules, and issue resolution
- Integrate metadata, data lineage, classification, and ownership directly into platform design patterns
- Advance AI governance for autonomous systems by enforcing data privacy and model lifecycle controls
- Establish AIOps benchmarks to ensure traceability, quality assessment, incident preparedness, and cost controls
- Work closely with product and stakeholder teams to uncover, rank, and resolve technical risks
- Keep current with regulatory, security, and compliance demands to align solutions with required standards
Work Arrangement
Hybrid — Canada, Spain, Switzerland, United Kingdom, United States
