Senior Director, Data Quality

Scotiabank•Dallas, TX

2d•Onsite

About The Position

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.

Requirements

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.

Responsibilities

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 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.