Data Quality Lead

M&T Bank•Baltimore, MD

6h•$85,800 - $143,000•Hybrid

About The Position

Leads and supports enterprise data quality efforts for the Customer 360 ecosystem, with a primary focus on ensuring the accuracy, completeness, consistency, timeliness, and reliability of customer data used for analytics, marketing, customer communications, customer journeys, personalization, regulatory reporting, and operational processes. The role is a key member of the Data Quality team responsible for monitoring, assessing, and improving data quality across the Enterprise Customer 360 platform, including Amperity, Snowflake, source applications, and downstream consumer systems. It partners with business stakeholders, technology and data engineering teams, data governance, marketing, analytics, and operational partners to identify, investigate, prioritize, and resolve issues affecting enterprise customer information. The successful candidate will use analytical skills and investigative research, root cause analysis, testing, issue management, reporting, and continuous monitoring to help ensure customer data remains trusted and fit for purpose across the enterprise. The position also supports platform enhancements, source data recertifications, monitoring initiatives, and the use of artificial intelligence and automation to improve data quality processes, controls, and operational efficiency. The position is hybrid, with four days onsite and one day remote, based at one of M&T's corporate locations.

Requirements

Bachelor's degree and at least 3 years of related experience, or, in lieu of a degree, a combined minimum of 7 years of higher education and/or work experience, including at least 3 years of related experience.
Experience with one or more database technologies.
Expert-level Structured Query Language (SQL), including advanced SQL.
Experience collecting and managing metadata.
Experience working in Data Governance and Data Quality programs at large, complex organizations.
Experience performing complex data analysis, testing, validation, and troubleshooting.
Strong SQL and data investigation skills.
Experience with large-scale enterprise data platforms and data warehouses.
Experience creating dashboards, scorecards, reports, and data quality metrics.
Strong root cause analysis and problem-solving abilities.
Excellent written and verbal communication skills, including the ability to translate technical findings into business-friendly recommendations.
Ability to manage multiple priorities and work in a fast-paced environment.

Nice To Haves

Experience with the Amperity Customer 360 platform.
Experience supporting Customer 360, Master Data Management (MDM), Customer Data Platforms (CDP), or identity resolution solutions.
Experience with Snowflake.
Experience with Monte Carlo or comparable data observability platforms.
Financial services industry experience.
Experience with enterprise data governance and data stewardship programs.
Experience developing data quality frameworks, controls, and monitoring solutions.
Experience supporting marketing, customer communications, personalization, and customer journey initiatives.
Knowledge of customer identity resolution concepts, householding, customer matching, and golden record methodologies.
Experience leveraging AI and automation for data analysis, testing, monitoring, or issue management.
Demonstrated intellectual curiosity and a passion for understanding and improving data.
Proven ability to influence change across cross-functional business and technology teams.

Responsibilities

Support enterprise-wide data quality initiatives across the Customer 360 environment and help ensure customer data is accurate, complete, consistent, timely, and reliable.
Serve as a subject matter expert for customer data domains and Customer 360 processes, including customer identity resolution, golden record construction, customer hierarchies, and customer data integration.
Partner with data governance and business teams to define and maintain critical data elements, data quality rules, thresholds, and controls.
Monitor customer data quality metrics and identify emerging trends, risks, and opportunities for improvement.
Perform ongoing data quality monitoring using Monte Carlo and other monitoring platforms; analyze alerts, anomalies, trends, and exceptions to determine root causes and business impact.
Design and maintain monitoring rules and data quality controls aligned with organizational standards and objectives.
Develop dashboards, scorecards, and executive reporting to communicate data quality health across the Customer 360 ecosystem.
Lead data quality testing for system enhancements, platform upgrades, new source integrations, and process changes; develop and execute test strategies, test cases, validation procedures, and reconciliations.
Perform certification and recertification of source system data contributing to Customer 360.
Validate customer identity resolution outcomes, attribute mappings, transformation logic, and business rule implementations.
Ensure data quality requirements are incorporated into project delivery and change management processes.
Conduct data analysis, research, and troubleshooting to identify root causes of data quality issues; lead enterprise investigations involving multiple systems, processes, and stakeholder groups.
Coordinate issue resolution across business, technology, analytics, and vendor teams; document findings, remediation plans, risks, and outcomes.
Track data quality issues through resolution and validate that corrective actions address root causes.
Support data quality activities for the Amperity Customer 360 platform and its customer identity, profile, and audience management capabilities.
Use Snowflake for data analysis, reconciliation, monitoring, and warehouse-based investigations; collaborate with data engineering teams to validate pipelines, transformations, integrations, and downstream data consumption.
Support customer data availability and usability for analytics, campaign management, customer communications, and journey orchestration.
Identify opportunities to improve data quality processes, controls, automation, and operational effectiveness; leverage AI and analytical tools to improve anomaly detection, issue identification, root cause analysis, documentation, testing, and reporting.
Recommend enhancements to monitoring frameworks, reporting capabilities, and data quality governance practices, and contribute to enterprise data quality standards and best practices.
Partner with business and technology stakeholders to assess risks and prioritize improvements; communicate complex findings to technical and non-technical audiences, facilitate remediation and operational readiness discussions, and provide recommendations that improve customer data reliability.