Technical Program Manager - MLOps, Data Governance

Job Description

Work Mode: Hybrid (2 days per week in-person at Toronto office preferred)

Skills required:

· 10–12 years in technical program/project management with at least 3–5 years in data platforms and AI/ML operations.

· Strong understanding of data architectures (lake/lakehouse, warehouse, streaming), data governance, and MLOps/ModelOps concepts.

· MLOps/AI: Azure ML, SageMaker, Vertex AI; MLflow, model registry, feature stores, drift/fairness/explainability tools.

· Data Governance: Purview, Collibra, Alation; data lineage, cataloging, DQ tooling.

· Orchestration and CI/CD: Airflow, Prefect, dbt; GitHub Actions/Azure DevOps/Jenkins; Terraform/Bicep/CloudFormation.

· Monitoring/Observability: Prometheus/Grafana, cloud-native monitors, logging, data quality monitors, model monitoring.

· Cloud & Data: Azure (Synapse, Fabric), AWS (S3/Glue/Redshift), GCP (BigQuery/Dataflow), Databricks, Snowflake.

· Proven experience embedding security/privacy-by-design and RAI principles into delivery and ops.

· Excellent stakeholder management, vendor management, and executive communication skills.

Roles and responsibilities

· Program Delivery Leadership

· Own end-to-end delivery of data platform and AI/ML operational initiatives discovery design implementation hypercare steady-state operations.

· Maintain multi-quarter roadmap, backlog and release trains (Scrum, Kanban, SAFe), run standups, PI planning, demos and retros.

· Manage dependencies across data ingestion, storage processing, cataloging, lineage, access, MLOps pipelines and app integrations.

· Orchestrate cross-functional squads.

· Data Engineering, Platform SRE, Security, Risk, Legal and Business to deliver secure, governed and compliant data capabilities and AI services at scale.

· Own roadmaps, delivery governance, risk controls, release management and post-production reliability for data, AI workloads and ensuring Responsible AI principles are codified into day-to-day operations.

· Platform Technical Ownership

· Partner with Platform Engineering

· SRE to evolve the data platform reference architecture.

· Drive integration and operationalization of MLOps and Model Ops practices.

· Oversee environment strategy (dev test stage prod), IaC-driven provisioning, cost guardrails and performance SLAs.

· Responsible AI Data Governance.

· Embed Responsible AI guardrails into SDLC and runtime model cards, fairness bias checks, explainability, human-in-the-loop, monitoring drift and incident response.

· Operationalize data governance meta data catalog, lineage, PII classification, DLP, RBAC (Role-Based Access Control), ABAC (Attribute-Based Access Control), data quality SLAs, retention deletion schedules.

· Align with privacy, security and regulatory frameworks (e.g. privacy laws, model risk management and AI assurance frameworks).

· Risk and Compliance Controls

· Maintain risk register, control library, audit trail, approvals and evidence for releases and model lifecycle events.

· Run change advisory (CAB) workflows for platform and model changes ensure traceability from requirements to deployment and monitoring.

· Stakeholder Management Communication.

· Translate business outcomes into measurable platform and AI service capabilities, SLIs and SLOs.

· Provide executive-level status (OKRs, KPIs, burn-up down, RAID, budget vs. actuals)

Certifications (nice-to-have):

· PMP/Prince2, CSM/SAFe, Azure/AWS/GCP data/AI, Databricks/Snowflake, Governance/Privacy.

Requirements
Work Mode: Hybrid (2 days per week in-person at Toronto office preferred) Skills required: • 10–12 years in technical program/project management with at least 3–5 years in data platforms and AI/ML operations. • Strong understanding of data architectures (lake/lakehouse, warehouse, streaming), data governance, and MLOps/ModelOps concepts. • MLOps/AI: Azure ML, SageMaker, Vertex AI; MLflow, model registry, feature stores, drift/fairness/explainability tools. • Data Governance: Purview, Collibra, Alation; data lineage, cataloging, DQ tooling. • Orchestration and CI/CD: Airflow, Prefect, dbt; GitHub Actions/Azure DevOps/Jenkins; Terraform/Bicep/CloudFormation. • Monitoring/Observability: Prometheus/Grafana, cloud-native monitors, logging, data quality monitors, model monitoring. • Cloud & Data: Azure (Synapse, Fabric), AWS (S3/Glue/Redshift), GCP (BigQuery/Dataflow), Databricks, Snowflake. • Proven experience embedding security/privacy-by-design and RAI principles into delivery and ops. • Excellent stakeholder management, vendor management, and executive communication skills. Roles and responsibilities • Program Delivery Leadership • Own end-to-end delivery of data platform and AI/ML operational initiatives discovery design implementation hypercare steady-state operations. • Maintain multi-quarter roadmap, backlog and release trains (Scrum, Kanban, SAFe), run standups, PI planning, demos and retros. • Manage dependencies across data ingestion, storage processing, cataloging, lineage, access, MLOps pipelines and app integrations. • Orchestrate cross-functional squads. • Data Engineering, Platform SRE, Security, Risk, Legal and Business to deliver secure, governed and compliant data capabilities and AI services at scale. • Own roadmaps, delivery governance, risk controls, release management and post-production reliability for data, AI workloads and ensuring Responsible AI principles are codified into day-to-day operations. • Platform Technical Ownership • Partner with Platform Engineering • SRE to evolve the data platform reference architecture. • Drive integration and operationalization of MLOps and Model Ops practices. • Oversee environment strategy (dev test stage prod), IaC-driven provisioning, cost guardrails and performance SLAs. • Responsible AI Data Governance. • Embed Responsible AI guardrails into SDLC and runtime model cards, fairness bias checks, explainability, human-in-the-loop, monitoring drift and incident response. • Operationalize data governance meta data catalog, lineage, PII classification, DLP, RBAC (Role-Based Access Control), ABAC (Attribute-Based Access Control), data quality SLAs, retention deletion schedules. • Align with privacy, security and regulatory frameworks (e.g. privacy laws, model risk management and AI assurance frameworks). • Risk and Compliance Controls • Maintain risk register, control library, audit trail, approvals and evidence for releases and model lifecycle events. • Run change advisory (CAB) workflows for platform and model changes ensure traceability from requirements to deployment and monitoring. • Stakeholder Management Communication. • Translate business outcomes into measurable platform and AI service capabilities, SLIs and SLOs. • Provide executive-level status (OKRs, KPIs, burn-up down, RAID, budget vs. actuals) Certifications (nice-to-have): • PMP/Prince2, CSM/SAFe, Azure/AWS/GCP data/AI, Databricks/Snowflake, Governance/Privacy.