Description
We're hiring a Lead Data Governance & Quality Analyst to strengthen how we define, manage, and trust enterprise Service & Operational data across platforms such as ServiceNow, Datadog, and other IT-related systems.
As our Lead Data Governance & Quality Analyst, you will:
- Lead governance execution for Service & Operational data domains, ensuring consistent definitions, ownership, and controls.
- Develop and maintain data quality standards: profiling, rule design, thresholds, completeness/accuracy/timeliness checks, and automated monitoring/alerting for priority datasets.
- Define and maintain data product expectations/contracts and partner with engineering to implement controls and alerts to ensure compliance.
- Partner with Data Owners and Stewards to define critical data elements, business glossaries, and data product/service expectations.
- Create and manage DQ scorecards and reporting, tying issues to business impact and tracking progress against KPIs.
- Run data issue management with ITSM-style rigor: intake, triage, severity/priority definitions, root-cause analysis coordination, remediation tracking, and post-fix validation.
- Support metadata management: lineage, definitions, classifications, retention, and usage documentation in data catalog tools.
- Ensure compliance and responsible data use by applying privacy/security classifications, access controls, and audit-ready documentation.
- Influence upstream processes to prevent defects and reduce rework.
- Contribute to governance operating model: standards, playbooks, training, and change management to increase adoption across teams.
- Facilitate governance forums and drive decisions on definitions, quality thresholds, and prioritization.
To be our Lead Data Governance & Quality Analyst, you will likely have:
- 5+ years in 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 implementing data quality controls and partnering with teams to operationalize them.
- Strong knowledge of data governance concepts: data ownership/stewardship, CDEs, metadata, lineage, data lifecycle, and controls.
- Advanced SQL skills: comfort analyzing data in relational warehouses/lakes, ability to work with semi-structured data.
- Working knowledge of data pipelines and common failure modes and ability to partner effectively with data engineering.
- Experience working cross-functionally with business partners and technical teams.
- Strong documentation, facilitation, communication, and stakeholder management skills; ability to drive outcomes without direct authority.
What Success Looks Like (First 6–12 Months):
- Established clear ownership and definitions for priority Service & Operational datasets and critical data elements.
- Implemented automated DQ monitoring and scorecards for key domains with measurable improvements in quality KPIs.
- Reduced recurring DQ incidents through effective RCA coordination and upstream prevention controls.
- Increased adoption of the catalog/glossary and governance workflows across teams; established a recurring governance rhythm with decision logs and prioritized backlog.
What’s in it For You?
- Hybrid Work Model: flexible hybrid working environment.
- Flexibility & Work-Life Balance: supportive workplace policies.
- Career Development and Growth: culture of continuous learning and skill development.
- Industry Competitive Benefits: comprehensive benefit plans.
- Culture: globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more.