Manager, Data Quality Assurance (Remote)

JOB SUMMARY
As a member of the Internal Assurance team, you will play a critical role in translating data governance and controls standards into audit and consulting projects that improve data trust and usability across the enterprise. This role will help ensure that downstream business processes, analytics, reporting, and AI initiatives consume accurate, complete, consistent and valid data by implementing data quality controls, enabling Cloud Data Governance & Catalog (CDGC) capabilities, and driving visibility through data quality metrics and scorecards. This role is hands-on and execution-focused, requiring close collaboration with data stewards, governance leads, data engineers, and application teams to proactively identify, resolve, and prevent data quality issues at scale.
KEY RESPONSIBILITIES

Team Translate business-defined data quality rules into technical implementations using techniques including, but not limited to, SQL validations, profiling rules, scorecards, and pipeline controls.
Implement data validation, cleansing, and standardization across enterprise data domains to prevent invalid data from propagating downstream.
Partner with functional teams to implement and monitor data quality controls across our master data domains.
Execute enterprise data quality frameworks, including rule standardization, threshold management, and alignment to critical data elements (CDEs).
Perform data profiling and validation activities, including completeness checks, duplicate analysis, pattern validation, referential integrity checks, and anomaly detection.
Configure and support CDGC capabilities, including dataset onboarding, metadata ingestion, catalog enrichment, governed asset management, and business glossary mapping.
Develop, enhance, and maintain data quality rule mapping, scorecards, dashboards, and data health visibility within CDGC and related reporting tools.
Support CDE classification, governed data asset onboarding, and data product registration within CDGC and data marketplace.
Support and validate technical and business data lineage across our master data, analytics, and downstream platforms for traceability and impact analysis.
Perform root cause analysis (RCA) over data quality errors to identify if they stem from system bugs or user entries, validate fixes through regression testing, and recommend structural and upstream process improvements to prevent recurring issues.

EDUCATION & EXPERIENCE
REQUIRED:

A minimum of 8 years of related experience with a Bachelor's degree in Computer Science, Information Systems, Data Engineering, Statistics or a related field; or 6 years of related experience with a Master's degree; or 3 years of related experience with a PhD; or an equivalent combination of education and experience.
Hands-on experience in data quality, metadata management, master data management, data engineering, or governance-focused technical roles.
Strong hands-on experience with Informatica CDGC, including metadata curation, dataset onboarding, catalog management, data quality rule mapping, and data quality visibility.
Hands-on experience with Purview, Alation, Collibra, CluedIn, or similar data quality solutions for profiling, validation, rule implementation, scorecards, and monitoring.
Strong working knowledge of master data domains, including customer, supplier, and finance, with experience supporting data quality, governance, and lifecycle management processes across ERP environments.
Solid experience with scripting languages (Python or R) for automation.
Familiarity with ETL tools, data profiling software, or cloud data warehouses (e.g., Azure Data Stack, Microsoft Fabric, Azure Databricks).
Experience working with business glossaries, CDEs, data lineage, and governed metadata management.
Strong SQL skills and experience querying databases, developing or supporting ETL, Intelligent Data Management Cloud (IDMC), and SQL-based data pipelines.
Experience performing data profiling, data validation, issue investigation, RCA, regression testing, and remediation tracking.
Strong analytical, problem-solving, and stakeholder collaboration skills.
Experience in translating complex, technical data errors into actionable insights for nontechnical stakeholders.

PREFERRED:

Experience with enterprise master data management initiatives.
Experience building or supporting data quality scorecards and dashboards using Power BI or similar visualization platforms.
Experience with cloud data platforms such as SILK, Azure Data Stack, Microsoft Fabric, Azure Databricks, or equivalent modern data environments.
Familiarity with master data management (MDM) concepts including match/merge, survivorship, validation rules, and reference data management.
Experience supporting data product enablement, self-service analytics, or data marketplace initiatives.
Basic scripting experience using Python, Spark, or related technologies.

KEY COMPETENCIES

Data Quality Management: Ensures data accuracy, completeness, consistency, and integrity through the implementation of data quality controls, validation rules, monitoring, and remediation processes.
Data Governance: Supports enterprise data governance practices by maintaining data standards, metadata, business glossaries, critical data elements (CDEs), and data lineage to improve trust and usability.
Technical & Analytical Expertise: Applies SQL, data profiling tools, governance platforms, and analytical techniques to identify issues, evaluate data health, and implement effective solutions.
Root Cause Analysis & Problem Solving: Investigates data quality issues, identifies underlying causes, validates corrective actions, and drives sustainable process improvements to prevent recurrence.
Reporting & Data Visualization: Develops and maintains data quality metrics, scorecards, dashboards, and reporting tools that provide visibility into data health and governance performance.
Cross-Functional Collaboration: Partners effectively with data stewards, engineers, governance leaders, and business stakeholders to implement data quality initiatives and achieve organizational objectives.
Communication & Influence: Communicates complex technical concepts and data issues clearly to diverse audiences, driving understanding, accountability, and adoption of best practices.
Continuous Improvement: Identifies opportunities to enhance data quality, governance processes, and operational efficiency through ongoing evaluation, innovation, and process optimization.

CORE VALUES
Step Up For Students believes strongly in two key core values to carry out their mission of “We empower families to pursue and engage in the most appropriate learning options for their children.”:

Everyone is an asset.
Every event is an improvement opportunity.

To maintain a positive and effective organizational culture, employees are expected to demonstrate these two core values in their everyday work.
WORKING CONDITIONS & EQUIPMENT USE

Work is performed indoors in a typical office environment - not substantially exposed to adverse environmental conditions.
Must be able to lift up to fifteen (15) pounds.

Frequent use of office machines to include telephone, computer, and printer.