Sr Data Quality Engineer

We are seeking a Senior Data Quality Lead to establish and own the data quality practice for an enterprise data platform built on Microsoft Fabric. This individual will serve as the primary authority on data quality, shaping standards, validation methodologies, certification processes, and governance integration to ensure trusted, audit-ready data throughout the data lifecycle.

This is a highly visible opportunity to build a data quality discipline from the ground up within a fast-paced financial services environment. The ideal candidate will have deep expertise in data testing, reconciliation, governance, and enterprise data quality frameworks, with a proven track record of implementing scalable quality controls across modern cloud-based data platforms.

Key Responsibilities

• Architect and maintain the enterprise data quality framework, including validation rules, acceptance thresholds, exception management workflows, and escalation procedures across Bronze, Silver, and Gold data layers.
• Design and implement source-to-target reconciliation processes and regression testing frameworks that provide the evidence required for production certification of curated datasets.
• Integrate data quality controls, certification status, lineage, and governance processes into Microsoft Purview to create a unified enterprise governance experience.
• Establish enterprise-wide visibility into data health through monitoring, reporting, and observability capabilities that track quality metrics, reconciliation outcomes, and certification readiness.
• Define and maintain a data quality rule taxonomy that categorizes validations by completeness, accuracy, consistency, timeliness, uniqueness, severity, and remediation requirements.
• Establish Bronze-layer ingestion standards, including schema validation, source freshness requirements, row count verification, and null-value threshold controls.
• Design Silver-layer quality controls that validate transformation logic, enforce referential integrity, and identify records that fail business rule requirements.
• Define Gold-layer certification criteria and production readiness sign-off processes in collaboration with business stakeholders and data engineering teams.
• Build and maintain automated reconciliation workflows across critical source systems, including loan servicing, accounting, portfolio management, and deal management platforms.
• Design and implement data quality monitoring and reporting strategies, including KPIs such as rule pass/fail rates, exception volumes, reconciliation variances, and time-to-resolution metrics.
• Establish exception management processes covering issue triage, ownership assignment, remediation SLAs, escalation paths, and continuous improvement initiatives.
• Conduct root cause analysis for recurring data quality issues and maintain remediation playbooks and framework documentation to preserve institutional knowledge.
• Partner closely with data engineering, governance, analytics, and business teams to ensure quality standards are embedded throughout the data lifecycle.
• Drive continuous improvement of data quality practices as the platform and business requirements evolve.

Required Qualifications

• Proven experience building or leading enterprise data quality programs, including framework development, rule taxonomy creation, certification processes, and governance integration.
• Extensive hands-on experience with data testing, validation, reconciliation, and data quality controls.
• Experience supporting modern cloud-based data platforms, preferably Microsoft Fabric, Azure Data Lake, Azure Synapse, or comparable environments.
• Strong understanding of medallion architecture concepts (Bronze, Silver, Gold).
• Experience with Microsoft Purview or similar data governance and cataloging platforms.
• Knowledge of data lineage, metadata management, certification workflows, and governance best practices.
• Experience working with financial services, banking, lending, private credit, accounting, or investment management data.
• Understanding of regulatory, audit, and reconciliation requirements within highly governed environments.