Director, Data Engineering, International Banking, Toronto, Mexico, Colombia, Peru

Scotiabank•Toronto, ON

24d•Hybrid

About The Position

The Director, DataOps is a senior technology leadership role accountable for the operational excellence, reliability, security, scalability, and cost efficiency of the Azure Databricks platform and related data operations across Mexico, Caribbean, Chile, Peru, and Uruguay. This role leads a distributed team responsible for production data pipeline operations, CI/CD enablement, platform onboarding, observability, incident management, governance controls, and continuous improvement of the Lakehouse operating model. The Director will partner closely with Data Engineering, Architecture, Platform Engineering, Cybersecurity, Risk, Compliance, and business stakeholders to ensure Databricks-based data products are delivered safely, reliably, and in alignment with regional priorities and enterprise standards.

Requirements

Bachelor’s degree in computer science, Data Engineering, Information Technology, or a related field; Master’s degree preferred.
10+ years of experience in data engineering, platform engineering, DevOps, SRE, or DataOps, including at least 5 years in a leadership role managing distributed technical teams.
Proven experience operating enterprise-scale data platforms in production, including incident management, release management, monitoring, SLA/SLO governance, and continuous improvement.
Deep hands-on knowledge of Azure Databricks, Spark, Delta Lake, Databricks Workflows/Jobs, workspace administration, cluster policies, runtime management, and integration with Azure data services.
Strong expertise in CI/CD and DevSecOps for data workloads, including Git-based development, automated testing, deployment pipelines, release gates, code quality controls, and rollback mechanisms.
Demonstrated ability to implement security, privacy, compliance, auditability, identity, access, and data governance controls in cloud data environments.
Experience with infrastructure-as-code, preferably Terraform, and monitoring/observability solutions such as Azure Monitor, Log Analytics, Databricks system tables, or equivalent tools.
Relevant certifications preferred, such as Azure Data Engineer Associate, Azure Solutions Architect, Azure DevOps Engineer, Databricks certifications, Terraform Associate, or equivalent experience.
Strong leadership, communication, and stakeholder management skills.
Proficiency in programming languages such as Python, SQL, or Scala.
Experience leading cost optimization and capacity planning for cloud data platforms, including budget controls, usage analytics, workload placement, and performance tuning.

Nice To Haves

Experience operating in highly regulated, multi-country, multicultural environments, preferably in financial services or banking.
Ability to travel regionally as required.
Spanish and English fluency preferred.
Strong executive communication skills, with the ability to simplify complex platforms, risk, and operational topics for senior leadership and non-technical stakeholders.

Responsibilities

Lead, coach, and scale a regional DataOps organization across Mexico, Caribbean, Chile, Peru, and Uruguay, with clear accountability for production operations, engineering discipline, service reliability, and continuous improvement.
Define operating objectives, performance expectations, delivery metrics, and talent development plans for engineers, operations leads, and platform support roles.
Coordinate priorities across countries, time zones, and partner teams, ensuring transparent execution, consistent standards, and timely escalation of risks, blockers, and service-impacting issues.
Define and implement the DataOps operating model for Azure Databricks, including platform onboarding, environment management, release controls, production support, change management, and operational governance.
Drive automation across the data delivery lifecycle, including CI/CD pipelines, infrastructure-as-code, automated testing, deployment gates, code promotion, rollback patterns, and release traceability.
Establish and monitor service-level objectives for data pipelines and platform operations, including availability, data freshness, latency, job success rates, incident response, recovery time, and data quality.
Own the operational readiness of Azure Databricks workspaces, clusters, jobs, workflows, notebooks, libraries, secrets, service principals, and production deployment patterns.
Partner with Platform Engineering and Architecture to define secure, scalable workspace patterns, environment separation, cluster policies, runtime standards, capacity planning, and platform guardrails.
Build a strong observability practice for pipelines and platform services, including monitoring, alerting, logging, operational dashboards, root-cause analysis, and problem management.
Oversee the design, deployment, and optimization of cloud-based data infrastructure supporting Azure Databricks, including secure connectivity, storage integration, workload isolation, and scalable compute patterns.
Lead FinOps discipline for Databricks workloads, including cost transparency, tagging, budget controls, rightsizing, auto-termination, utilization reporting, capacity forecasting, and optimization of compute spend.
Ensure compliance with enterprise cloud, cybersecurity, privacy, data residency, and regulatory requirements, coordinating with control functions and regional stakeholders as needed.
Define and enforce governance practices for Databricks and lakehouse assets, including Unity Catalog adoption, access management, data lineage, auditability, ownership models, and production data controls.
Establish secure operating patterns for service principals, secrets management, privileged access, environment segregation, and production job execution.
Partner with Cybersecurity, Risk, Compliance, and Architecture teams to ensure controls are embedded early in the delivery lifecycle and continuously validated through operational reporting.
Serve as the operational owner and escalation point for Databricks DataOps across regional, global, platform, architecture, security, and business stakeholder groups.
Communicate operational health, delivery progress, risks, incidents, remediation plans, platform dependencies, and investment needs to senior leadership.
Establish executive-ready KPIs and dashboards covering platform adoption, pipeline reliability, SLA performance, data quality, incidents, cost efficiency, onboarding progress, and engineering productivity.
Stay current with Databricks, Azure, DataOps, DevSecOps, FinOps, observability, and data engineering practices, translating relevant capabilities into practical standards for the organization.
Develop reusable playbooks, templates, reference architectures, onboarding guides, runbooks, support models, and engineering standards to accelerate safe and consistent adoption of Databricks.