Manager, Data Quality Assurance

Step Up For Students•Remote, OR

1d•$102,000 - $130,000•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.