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$154K–$225K is typical
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From 2,958 postings in the last year. Stated ranges, not salaries paid.
Dallas, TX, US, 75219
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$154K–$225K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 2,958 postings in the last year. Stated ranges, not salaries paid.
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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: o Databricks, Delta Lake, Unity Catalog, workflows, and Lakehouse data engineering patterns o Data quality platforms, profiling tools, observability frameworks, rule engines, and monitoring capabilities o Metadata-driven controls, catalog integration, lineage-aware quality monitoring, and data contract implementation o Strong understanding of data quality dimensions, critical data elements, quality scorecards, SLAs/SLOs, and issue management workflows o Practical experience embedding quality controls into batch, streaming, and orchestration workflows o Cloud platform experience, with Azure preferred • Deep expertise in data quality engineering, observability, profiling, rule management, monitoring, and remediation at enterprise scale. • Strong understanding of data governance, metadata, lineage, security, privacy, and regulatory compliance as they relate to data quality controls. • Proven ability to work directly with engineers, data engineers, architects, data owners, stewards, and risk partners to deliver measurable improvements in data trust. • Exceptional written communication skills with strong attention to technical accuracy, operational discipline, and executive-ready reporting. Interested?
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 Data Quality Strategy & Engineering Execution: • 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: o Data profiling, quality rule authoring, and rules lifecycle management o Completeness, accuracy, validity, uniqueness, timeliness, consistency, and freshness checks o Quality thresholds, SLOs, scorecards, and certification criteria for critical data assets o 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. Data Observability, Monitoring & 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. 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.
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We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
Requisition ID: 271992
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
Data Quality Strategy & Engineering Execution:
• 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:
o Data profiling, quality rule authoring, and rules lifecycle management
o Completeness, accuracy, validity, uniqueness, timeliness, consistency, and freshness checks
o Quality thresholds, SLOs, scorecards, and certification criteria for critical data assets
o 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.
Data Observability, Monitoring & 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.
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:
o Databricks, Delta Lake, Unity Catalog, workflows, and Lakehouse data engineering patterns
o Data quality platforms, profiling tools, observability frameworks, rule engines, and monitoring capabilities
o Metadata-driven controls, catalog integration, lineage-aware quality monitoring, and data contract implementation
o Strong understanding of data quality dimensions, critical data elements, quality scorecards, SLAs/SLOs, and issue management workflows
o Practical experience embedding quality controls into batch, streaming, and orchestration workflows
o Cloud platform experience, with Azure preferred
• Deep expertise in data quality engineering, observability, profiling, rule management, monitoring, and remediation at enterprise scale.
• Strong understanding of data governance, metadata, lineage, security, privacy, and regulatory compliance as they relate to data quality controls.
• Proven ability to work directly with engineers, data engineers, architects, data owners, stewards, and risk partners to deliver measurable improvements in data trust.
• Exceptional written communication skills with strong attention to technical accuracy, operational discipline, and executive-ready reporting.
Interested?
If your experience is closely related but doesn’t align perfectly with every qualification, we do encourage you to apply - you might be the right candidate for this or other roles at Scotiabank!
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. If you require technical assistance, please click here. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in 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.
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Dallas, TX, US, 75219
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