Lead Data Quality Consultant

Position : Lead Data Quality Analyst Role Summary The project data quality program identifies issues through an automated rules engine and diagnoses them through root cause analysis, but a gap exists between diagnosis and resolution: someone must translate technical findings into business-ready communication, drive data owners to act, and confirm the fix actually worked. This role owns that last mile. It sits downstream of rule creation and RCA and is accountable for closing the loop — packaging findings, validating them with the business, securing remediation commitments from data owners, and re-running data quality checks to confirm issues are resolved before they are marked closed. Strong enough in data and SQL to understand and repackage RCA output, and strong enough in stakeholder management to hold data owners accountable to fix dates and confirm resolution with evidence, not assumptions. How This Role Fits the DQ Workflow • Builds and deploys new data quality rules into the rules engine, generating raw findings and rule violations across core Snowflake tables. • Perform root cause analysis on those findings to determine whether the issue falls within project remediation purview or must be routed to an external data owner. • This role picks up the RCA output, determines the business-appropriate framing, validates findings with business stakeholders, and drives the remediation and re-validation cycle through to closure. Key Responsibilities: • Take root cause analysis output from team and convert it into clean, consistent, business-readable formats (briefs, one-pagers, tracker entries) tailored to the audience — technical or non-technical. • Validate findings with relevant business stakeholders before escalation, confirming the issue, its impact, and priority are correctly understood and agreed upon. • Identify and engage the appropriate data owner(s) for issues outside project direct remediation purview, and package a clear, actionable brief for each handoff. • Proactively follow up with data owners to secure remediation commitments, track them to resolution, and escalate stalled items through appropriate channels. • Coordinate with client to trigger re-runs of the relevant data quality rules once a data owner reports a fix, to independently verify the issue is resolved. • Maintain a single source of truth (e.g., Azure DevOps board or tracker) showing status of every DQ finding from initial detection through business validation, remediation, and confirmed closure. • Produce recurring status reporting and executive-ready summaries on open findings, aging issues, and remediation trends across data domains. • Flag recurring patterns or systemic issues back to the DQ rules and RCA team to inform new rule development or process improvements.