Data Governance & QA Lead

The MJ Companies•Carmel, IN

35d

About The Position

The Data Governance & QA Lead owns data quality for source systems such as the Customer Relationship Management system (CRM) and related Agency Management Systems (AMS). This role establishes standards, monitors data health, builds and maintains exception reporting, and drives timely correction—either directly or by coordinating with the accountable data owner. The Lead also reviews engineering pull requests for adherence to established standards and documentation quality, ensuring changes improve—not degrade—data integrity. Success requires strong attention to detail, process discipline, and professional communication that holds peers accountable without creating conflict.

Requirements

3+ years in data quality, data governance, or QA engineering (CRM domain highly preferred).
Skilled with SQL for profiling, validation, and remediation
Familiar with data quality/observability frameworks (e.g., Great Expectations, Fabric Data Quality, dbt tests) and monitoring/alerting.
Experience building exception reports/dashboards (Power BI/Fabric preferred) and managing issue queues to closure.
Understanding of data modeling, data lineage/metadata (e.g., Microsoft Purview), and data contracts.
Comfortable with Git‑based workflows (branching, PR review), CI/CD, and documentation standards (READMEs, runbooks, data dictionary).
Knowledge of privacy and compliance considerations (PII/PHI, HIPAA, SOC 2); role‑based access and change control.
Bachelor’s degree in Information Systems, Data/Computer Science, or related field—or equivalent practical experience.

Nice To Haves

insurance domain familiarity (EB or P&C).

Responsibilities

Serve as the point person for CRM data quality—accuracy, completeness, consistency, timeliness, and validity.
Define and maintain data quality rules, thresholds, and SLAs; align with business definitions and regulatory requirements.
Triage, troubleshoot, and correct data defects; facilitate remediation by data owners when changes must occur at the source.
Design, build, and maintain exception reports and dashboards; implement alerting for threshold breaches.
Prioritize and route exceptions; track remediation through to closure with root‑cause analysis and recurrence prevention.
Help to define coding, data‑modeling, and documentation standards for pipelines, integrations, and transformations.
Review pull requests for compliance with standards (naming, lineage, tests, data contracts, performance) and adequate documentation.
Partner with security and compliance on policies that protect PII/PHI; support SOC 2/HIPAA evidence gathering.
Facilitate data ownership/stewardship model; run governance routines (quality councils, defect reviews, and sign‑offs).
Work closely with product, operations, sales leadership, analytics, and engineering to align definitions and resolve issues.
Provide training and playbooks for data owners and engineers; promote a culture of quality and accountability.
Profile data sets; write validation queries and automated tests; create and maintain exception logic.
Remediate data issues directly or coordinate owner corrections; verify fixes and prevent recurrence.
Review PRs for standards, tests, and documentation; approve/require changes as appropriate.