Senior Director, Data Quality

Scotiabank; Dallas (TX); On-site; USD 180,000 - 250,000; Full time

Benefits offered by this job

Flexible benefits

Inclusive culture

Job summary

Scotiabank is seeking a Senior Director of Data Quality Engineering to lead enterprise data quality strategy and build scalable Lakehouse-based capabilities. You will align with data owners, engineers, governance, risk, and business leaders to deliver audit-ready controls and measurable improvements in data trust across analytics and regulatory reporting.

You will drive adoption of reusable data quality frameworks, while shaping an organization of data quality engineers and managers to achieve

Qualifications

• Bachelor’s degree in computer science, engineering, information technology, data management, or a related discipline.
• 12+ years of progressive experience in data engineering, data quality, data management, platform engineering, or related technology disciplines, including 7+ years leading engineering teams and 3+ years managing managers or senior technical leaders.
• Depth in financial services or other highly regulated industries, with demonstrated experience delivering audit-ready controls, regulatory reporting quality, operational risk reduction, and executive-level governance outcomes.
• Hands-on experience with Databricks, Delta Lake, Unity Catalog, workflows, and Lakehouse data engineering patterns.

Responsibilities

• Define and own the enterprise data quality engineering strategy, roadmap, and operating model aligned to Lakehouse architecture.
• Lead the design, delivery, and continuous improvement of scalable enterprise data quality capabilities, including profiling, rules management, and anomaly detection.
• Build and operate data observability capabilities for freshness, volume, schema drift, and distribution changes.
• Productize data quality capabilities as reusable platform services and promote data contracts with explicit quality expectations.
• Lead and develop a high-performing team of data quality engineers and managers.

Skills

Executive communication

Stakeholder management

Leadership

Data governance awareness

Education

Bachelor’s degree in computer science or related

Tools

Databricks

Delta Lake

Unity Catalog

Azure

Job description

Requisition ID: 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

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 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.

What You'll Do

Enterprise Data Quality Strategy & Engineering Leadership:

• 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.

Data Observability, Monitoring & Operational Trust

• 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.

Platform Productization, Adoption & Organizational Leadership

• 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.
• Build, lead, and develop a high-performing organization of data quality engineering managers, senior engineers, and specialists; establish talent plans, engineering practices, delivery discipline, and a culture of accountability and continuous improvement.
• Represent data quality engineering in executive forums by communicating strategy, delivery progress, operational risk, quality trends, investment priorities, and measurable business outcomes.

What You'll Bring

• Bachelor’s degree in computer science, engineering, information technology, data management, or a related discipline.
• 12+ years of progressive experience in data engineering, data quality, data management, platform engineering, or related technology disciplines, including 7+ years leading engineering teams and 3+ years managing managers or senior technical leaders.
• Depth in financial services or other highly regulated industries, with demonstrated experience delivering audit-ready controls, regulatory reporting quality, operational risk reduction, and executive-level governance outcomes.
• 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 monitoring, and data contract implementation
• Strong understanding of data quality dimensions, critical data elements, quality scorecards, SLAs/SLOs, and issue management workflows
• Practical experience embedding quality controls into batch, streaming, and orchestration workflows
• Cloud platform experience, with Azure preferred
• Proven enterprise leadership in data quality engineering, observability, profiling, rule management, monitoring, incident response, remediation, and continuous improvement at scale.
• Strong understanding of data governance, metadata, lineage, security, privacy, and regulatory compliance as they relate to data quality controls.
• Demonstrated ability to influence senior executives, architects, data owners, stewards, risk partners, and engineering teams to deliver measurable improvements in data trust, control effectiveness, and business confidence.
• Exceptional executive communication skills, with the ability to translate complex data quality, platform, and risk topics into clear business impact, investment priorities, operational metrics, and decision-ready recommendations.

Interested?

At Scotiabank, every employee is empowered to reach their fullest potential, respected for who they are and, embraced for their differences. That’s why we work to grow and diversify talent and engage employees in a performance-oriented culture.

What's in it for you?

Scotiabank wants you to be able to bring your best self to work – and life, every day. With a focus on holistic well-being, our many flexible benefit programs are designed to help support your unique family, financial, physical, mental, and social health needs.

#Dallas

Location(s): United States : Texas : Dallas

Scotiabank is a leading bank in the Americas. Guided by our purpose: "for every future", we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.

At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. We thank all applicants for a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.

Scotiabank is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by federal, state, or local law.