SVP, Chief Data and AI Engineering Officer

What you'll be doing Enterprise Data & AI Engineering Leadership

• Define & execute strategy:
  Lead the multi-year enterprise roadmap for data and AI engineering aligned with AI transformation and business priorities.
• Governance & stewardship:
  Establish and chair the enterprise data governance council; define data ownership, stewardship, and responsible AI governance frameworks.
• Architect for scale:
  Build modern, cloud-native data infrastructure—including storage, compute, streaming, and batch capabilities—to support high-throughput, low-latency AI workloads.
• Shape enterprise architecture:
  Develop logical architectures, semantic models, ontologies, and domain definitions tailored for AI consumption.

• Lead rapid experimentation and quick proof of concepts for priority AI use cases.
• Guide MVPs through scalable, production ready pathways.
• Lead hands on technical design, including model approach, data needs, and solution architecture in partnership with AI product teams.

• Build robust data pipelines:
  Oversee ingestion, transformation, and orchestration pipelines engineered for reliability, scalability, and reusability.
• Enable end-to-end MLOps:
  Implement enterprise capabilities such as feature stores, model registries, CI/CD, automated testing, deployment, and rollback.
• Operationalize responsible AI:
  Embed model observability, drift detection, performance monitoring, lineage, and audit trails to meet regulatory and ethical standards.

• Champion security & compliance:
  Ensure enterprise-level data security, privacy controls, access governance, and regulatory compliance across all platforms.
• Drive operational excellence:
  Lead Site Reliability Engineering (SRE), incident response, and capacity planning to meet availability and performance targets.
• Optimize cloud economics:
  Implement Fin Ops practices—right-sizing, autoscaling, workload optimization—to ensure cost-efficient platform operations.

• Deliver trusted data products:
  Lead the design of data products, catalogs, and self-service tooling that empower AI, analytics, and business teams.
• Lead organizational change:
  Drive data literacy, enablement, and cultural transformation to accelerate enterprise-wide AI readiness.
• Manage strategic partnerships:
  Oversee vendor relationships and evaluate build-vs-buy decisions across platforms, tools, and data services.
• Influence at the executive level:
  Communicate strategy, risk posture, and engineering outcomes in clear business terms to senior leadership.

Must Haves:

• Bachelor’s degree plus 15+ years of progressive experience in large-scale data management, platform engineering, or software engineering supporting AI/ML.
• 7-10+ years in senior leadership with proven experience scaling teams from