Issue Management &

- Triage : Monitor &
- Triage: Track and categorize incoming business data quality issues submitted via ServiceNow, Jira, email, or Microsoft Teams.

Lifecycle Management : Manage data incidents through their entire lifecycle, ensuring timely communication and SLA adherence. Data Investigation &

- Validation : Data Auditing: Perform cross-system data validation across up-to-date cloud data warehouses (BigQuery, Azure Synapse, Amazon Redshift) and reporting dashboards.

Root Cause Analysis : Investigate data mismatches, schema drift, incomplete transformations, and automated data quality rule violations.

Issue Resolution : Independently execute runbook-driven remediation for recurring/known issues; gather full technical context to escalate complex incidents to data engineering teams.



Documentation &

- Operational Excellence : Runbook Maintenance: Create, update, and maintain comprehensive standard operating procedures (SOPs), knowledge base articles, and runbooks.

Process Improvement: Identify recurring data quality trends and suggest proactive monitoring or alerting improvements. Problem-Solving : Proven ability to troubleshoot data discrepancies methodically and communicate findings clearly to both technical and non-technical stakeholders.

Documentation : Strong technical writing skills for maintaining SOPs and incident root-cause reports

📌 Data Quality Analyst (Hyderabad)
🏢 Niveus
📍 Hyderabad