Lead Data Governance & Quality Analyst

Job Requisition: Lead Data Governance & Quality Analyst Service & Operational Data

Location: Bangalore (Hybrid); collaborate with AMERS/EMEA/APAC stakeholders as needed.

Job Type:Full-time; Lead-level (hands-on Individual Contributor)

Were hiring aLead Data Governance & Quality Analystto strengthen how we define manage and trust enterprise Service & Operational data across platforms such as ServiceNow Datadog and other IT-related this role youll own governance execution and data quality practices for core Service & Operational domains (e.g. IT Service Management (Incident Problem Change Asset CMDB Request Knowledge SLAs/SLOs) observability telemetry (metrics logs events synthetics RUM). Where end-to-end service and operational metrics span multiple systems youll partner partner with Customer Experience and Engineering teams (e.g. Salesforce Azure DevOps) to align cross-domain definitions and quality requirements driving outcomes through collaboration with business owners data stewards product teams and engineering to ensure data iswell-defined measurable compliant and fit for purpose.

Youll translate policy into action by establishing clear data definitions ownership and measurable controls implementing data quality rules monitoring issue management workflows and governance operating rhythms that improve decision-making reduce risk and enable scalable analytics and AI.

About the Role

As our Lead Data Governance & Quality Analyst you will:

• Lead governance executionfor Service & Operational data domains (e.g. Incident/Problem/Change/Request Observability/Telemetry Availability/SLA/SLO) ensuring consistent definitions ownership and controls.

• Develop and maintain data quality (DQ) standards: profiling rule design thresholds completeness/accuracy/timeliness checks and automated monitoring/alerting for priority datasets.

• Define and maintain data product expectations / contracts (schema definitions freshness SLAs/SLOs usage guidance) and partner with engineering to implement controls and alerts to ensure compliance.

• Partner with Data Owners and Stewardsto define critical data elements (CDEs) business glossaries and data product/service expectations (e.g. incident categorization assignment group CI mapping SLA clocks telemetry tags).

• Create and manage DQ scorecards and reporting tying issues to business impact and tracking progress against KPIs; run a regular review cadence with owners/stewards to drive decisions and prioritization.

• Run data issue management with ITSM-style rigor: intake triage severity/priority definitions root-cause analysis coordination remediation tracking and post-fix validation; prevent recurrence via upstream controls and process improvements.

• Support metadata management: lineage definitions classifications retention and usage documentation in data catalog tools.

• Ensure compliance and responsible data useby applying privacy/security classifications (e.g. PII) access controls and audit-ready documentation.

• Influence upstream processes(data entry standards integrations reconciliation logic product changes) to prevent defects and reduce rework (e.g. mandatory fields controlled vocabularies CI normalization tag standards).

• Contribute to governance operating model: standards playbooks training and change management to increase adoption across teams.

• Facilitate governance forums(working sessions steward councils) and drive decisions on definitions quality thresholds and prioritization; maintain decision logs and backlog transparency.

• Partner closely with Service Management SRE/Observability Engineering Data & Analytics teams to align on definitions controls and overall plans.

About you:

To be our Lead Data Governance & Quality Analyst you will likely have:

• 5 yearsin data governance data quality analytics or related data management roles; experience in a lead capacity strongly preferred ideally with operational/ITSM/telemetry datasets.

• Demonstrated experience defining and implementingdata quality controls(profiling rules monitoring scorecards issue workflows) and partnering with teams to operationalize them.

• Strong knowledge ofdata governance concepts: data ownership/stewardship CDEs metadata lineage data lifecycle and controls and how they apply to event/ticketing/telemetry data.

• Advanced SQL skills: comfort analyzing data in relational warehouses/lakes ability to work with semi-structured data (e.g. JSON/log-like structures) is a plus.

• Working knowledge of data pipelines and common failure modes (APIs ELT/ETL streaming vs batch) and ability to partner effectively with data engineering.

• Experience working cross-functionally with business partners and technical teams (e.g. Service Management Engineering Architecture Product Compliance Security).

• Strong documentation facilitation communication and stakeholder management skills; ability to drive outcomes without direct authority.

• Comfortable being hands-on: profiling data writing SQL checks documenting definitions and running working sessions.

Preferred / Nice-to-Have

• Familiarity with governance frameworks and best practices (e.g. DAMA/DMBOK concepts).

• Experience with catalog/governance platform tools such as Alation or similar catalog/governance platforms.

• Experience with workflow tools (ADO ServiceNow).

• Experience with ServiceNow data model (Incident/Problem/Change/CMDB) and/or observability/telemetry (metrics/logs/traces) including entity/identity mapping concepts is a plus.

• Experience defining metrics for service reliability/operations (e.g. MTTR Change Failure Rate Availability SLA attainment) and standardizing calculation logic.

• Understanding of privacy/security practices (PII concepts) data retention and access governance.

• Experience supporting AI/ML or BI environments where data trust is critical.

What Success Looks Like (First 612 Months)

• Established clearownership and definitionsfor priority Service & Operational datasets and critical data elements (e.g. core ITSM practices (Incident/Change/Problem/Request) Assignment Groups CI mapping telemetry tags).

• Implemented automatedDQ monitoring and scorecardsfor key domains with measurable improvements in quality KPIs; introduced alerting and an agreed response process when thresholds are breached.

• Reduced recurring DQ incidents through effective RCA coordination and upstream prevention controls (e.g. validation rules standard vocabularies reconciliation improvements).

• Increased adoption of the catalog/glossary and governance workflows across teams; established a recurring governance rhythm with decision logs and prioritized backlog)

Why This Role Matters

Service & Operational data governance and quality are foundational to trustworthy reporting operational decisions and responsible AI. This role turns governance from a policy document into practical standards data contracts controls and measurable improvements that teams rely on every day.

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Whats in it For You

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