Data Quality Manager

Top Must haves:

• Hands-on experience with data testing, validation, reconciliation, and data quality controls.
• Experience working with financial services or banking data.
• Experience supporting Azure-based data environments (Fabric, Synapse, ADF, Databricks, Data Lake), but from a testing and quality perspective rather than a pure engineering perspective.

• Senior Data Quality Lead
• Location: Montgomery County, MD (Hybrid – 4 Days Onsite per Week)
• Employment Type: Full-Time Direct Hire
• Position Overview
• Our client is seeking a Senior Data Quality Lead to establish and own the data quality practice for an enterprise data platform built on Microsoft Fabric. This is a highly visible, hands-on opportunity to build data quality standards, validation frameworks, certification processes, and observability capabilities from the ground up.
• The ideal candidate will combine strong technical expertise with a deep understanding of data quality, data validation, and financial data environments. This individual will serve as the primary owner of the organization's data quality framework, ensuring trusted, reliable, and audit-ready data across the enterprise.
• This is a senior-level individual contributor role with significant influence across business and technology teams. The successful candidate will help define the future state of data quality while partnering closely with Data Engineering, Analytics, Finance, Risk, and Compliance stakeholders.
• Responsibilities
• Design and implement an enterprise-wide data quality framework, including validation rules, quality thresholds, exception management processes, and certification standards.
• Define and maintain data quality standards across multiple layers of the data platform, ensuring accuracy, completeness, consistency, timeliness, and reliability of business-critical data.
• Develop source-to-target reconciliation processes and automated validation controls supporting financial reporting, analytics, and operational decision-making.
• Establish data quality monitoring, observability, and reporting capabilities that provide visibility into data health, exceptions, and remediation activities.
• Partner with Data Engineering teams to implement automated quality checks, validation processes, and testing frameworks within enterprise data pipelines.
• Conduct data profiling, root cause analysis, and issue resolution activities to identify and remediate systemic data quality concerns.
• Collaborate with business stakeholders to define certification criteria, quality expectations, and production readiness standards for critical datasets.
• Develop and maintain documentation, operating procedures, and best practices related to data quality and validation activities.
• Evaluate and recommend data quality tools, frameworks, and technologies that support the organization's long-term data strategy.
• Required Qualifications
• Experience designing, implementing, or supporting enterprise data quality frameworks and validation processes.
• Strong experience with Microsoft Fabric, Azure Synapse Analytics, Azure Data Factory, Azure Data Lake, Databricks, or similar modern cloud data platforms.
• Advanced SQL skills with experience performing data validation, reconciliation, profiling, and root cause analysis.
• Experience working within financial services, banking, lending, accounting, or other highly regulated data environments.
• Strong understanding of dimensional modeling, data warehousing concepts, and enterprise reporting architectures.
• Experience developing automated data quality controls, testing processes, and monitoring solutions.
• Proficiency with Python and modern data engineering technologies.
• Excellent communication skills with the ability to partner effectively across technical and business teams.
• Preferred Qualifications
• Experience with Microsoft Fabric Lakehouse, Warehouse, Data Pipelines, OneLake, and Medallion Architecture.
• Experience with data quality and observability tools such as Great Expectations, Soda, Monte Carlo, Deequ, or similar platforms.
• Familiarity with data governance, metadata management, lineage, and cataloging concepts.
• Experience supporting audit, compliance, regulatory reporting, or control-focused environments.