Manager, Data Quality Engineering

Scotiabank•Dallas, TX

7d•Onsite

About The Position

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.

Requirements

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 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.
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.

Responsibilities

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.
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.