Manager, Data Quality Engineering

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Title: Manager, Data Quality Engineering

Requisition ID:

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

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!

Purpose

The Manager, Data Quality Engineering, is a hands-on engineering people leader responsible for designing, building, and scaling enterprise data quality capabilities on a modern Databricks Lakehouse platform. This role is critical to strengthening trust in data by embedding automated quality controls, observability, and remediation workflows across data pipelines, data products, and regulatory reporting processes.

You will partner with the Head and VP of Data Management Engineering to drive execution and adoption of enterprise data quality capabilities, including profiling, rule management, anomaly detection, quality scorecards, data observability, incident management, and quality controls integrated directly into Lakehouse engineering workflows.

What You'll Do

• Deliver the enterprise data quality engineering solution aligned to Lakehouse architecture, including Databricks, Delta Lake, Unity Catalog, and the Enterprise Data Catalog.
• Establish scalable data quality capabilities for:
  • 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
• Embed automated quality checks into end-to-end data pipelines, including Bronze, Silver, and Gold layers, so issues are detected early and prevented from flowing downstream.
• Partner with data owners, stewards, engineers, platform teams, and risk stakeholders to define quality expectations for critical data elements and data products.
• Drive adoption of reusable data quality patterns, templates, and APIs that make quality controls easy for engineering teams to implement at scale.
• Ensure data quality controls are measurable, auditable, and aligned with regulatory, reporting, and operational risk requirements.

• 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.

Data Quality Productization & Adoption

• Productize data quality capabilities as reusable platform services, including rule libraries, validation templates, metadata-driven controls, and self-service onboarding patterns.
• Ensure data contracts include explicit quality expectations such as schema, SLA/SLO, freshness, completeness, and acceptance criteria.
• Promote trusted, certified, and fit-for-purpose data assets by integrating quality signals into catalog, marketplace, and stewardship workflows.

What You'll Bring

• Bachelor’s degree in computer science, engineering, information technology, data management, or a related discipline.
• 7+ years of experience in data engineering, data quality, data management, or platform engineering, with 5+ years in a people leadership role.
• Experience in financial services or other highly regulated industries, including familiarity with auditability, controls, regulatory reporting, and operational risk expectations.
• Hands-on experience with:
  • Databricks, Delta Lake, Unity Catalog, workflows, and Lakehouse data engineering patterns
  • Data quality platforms, profiling tools, observability frameworks, rule engines, and monitoring capabilities
  • Metadata-driven controls, catalog integration, lineage-aware quality