Senior Director, Data Quality

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Title:

Senior Director, Data Quality

Requisition : 270860

Salary Range: -

Please note that the Salary Range shown is a guideline only. Salary offered may vary based on factors, including, but not limited to, the successful candidate’s relevant knowledge, skills, and experience.

Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.

Global Banking and Markets (GBM) is a leading Canadian Capital Markets and Investment Banking business with a growing platform in the US and Latin America, operating globally for over 100 years. Scotiabank’s strong U.S. presence provides our clients an important bridge to this key global market for trade and investment flows across the Americas and the world. Global Banking & Markets provides a full range of investment banking, credit and risk management products and services relevant to the financing and strategic development needs of our clients.

Our products include debt and equity financing, mergers & acquisitions, corporate banking, institutional equity sales, trading and research, fixed income products, derivatives, energy, foreign exchange and precious & metals. We also cross-sell the full range of wholesale products and services offered by the Scotiabank Group. Be part of an innovative, Global Capital Markets and Investment Banking business with a unique geographic footprint that puts capital to work for our clients across industries!

We work together to drive ambition for every future!

The Senior Director, Data Quality Engineering, is an enterprise engineering leader accountable for defining, building, and scaling data quality capabilities that strengthen trust in critical data across analytics, AI, regulatory reporting, and operational processes. This role owns the data quality engineering strategy, operating model, roadmap, and execution across a modern Databricks Lakehouse platform, ensuring quality controls are automated, observable, measurable, and embedded directly into the data lifecycle.

You will partner with senior technology, data, governance, risk, and business leaders to establish enterprise-wide data quality standards, engineering patterns, observability practices, and remediation workflows. You will lead managers and senior engineers responsible for capabilities including profiling, rule management, anomaly detection, quality scorecards, data contracts, incident management, and quality controls integrated into Lakehouse engineering workflows.

• Define and own the enterprise data quality engineering strategy, roadmap, and operating model aligned to Lakehouse architecture, including Databricks, Delta Lake, Unity Catalog, metadata services, and the Enterprise Data Catalog.
• Lead the design, delivery, and continuous improvement of scalable enterprise data quality capabilities, including:
  • Data profiling, quality rule authoring, and rules lifecycle management
  • Completeness, accuracy, validity, uniqueness, timeliness, consistency, and freshness checks
  • Quality thresholds, SLOs, scorecards, and certification criteria for critical data assets
  • Data quality issue detection, triage, ownership, remediation, and evidence capture
• Set engineering standards that embed automated quality checks into end-to-end data pipelines, including Bronze, Silver, and Gold layers, so issues are detected early, prevented from flowing downstream, and governed through repeatable controls.
• Influence and align data owners, stewards, engineers, platform teams, security, risk, audit, and senior business stakeholders on quality expectations for critical data elements, data products, and regulatory reporting processes.
• Drive adoption of reusable data quality frameworks, patterns, templates, APIs, and self-service onboarding models that make quality controls practical for engineering teams to implement at enterprise scale.
• Ensure data quality controls are measurable, auditable, policy-aligned, and supported by evidence required for regulatory, reporting, operational risk, and executive governance needs.

• Build and operate data observability capabilities that provide visibility into freshness, volume, schema drift, distribution changes, completeness, and reliability across critical pipelines.
• Implement automated profiling, anomaly detection, alerting, and monitoring to identify quality issues before they impact analytics, AI, reporting, or downstream business processes.
• Create quality dashboards, scorecards, and service-level indicators that help business and technology stakeholders understand data health, trends, and risk exposure.
• Lead root-cause analysis and continuous improvement efforts for recurring data quality issues, partnering with source system, pipeline, and product teams to eliminate defects at the source.

• Productize data quality capabilities as…