Lead Data Governance & Quality Analyst
Thomson Reuters · Gdańsk · PLN 178,500–PLN 331,500 (stated in the posting)
Job Requisition: Lead Data Governance & Quality Analyst – Service & Operational Data
Location: Gdansk (Hybrid); collaborate with AMERS/EMEA/APAC stakeholders as needed.
Job Type: Full-time; Lead-level (hands-on Individual Contributor)
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. In this role, you'll 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, you’ll 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 is well-defined, measurable, compliant, and fit for purpose.
You'll 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 execution for 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 Stewards to 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 use by 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+ 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 (profiling, rules, monitoring, scorecards, issue workflows) and partnering with teams to operationalize them.
- Strong knowledge of data 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 6–12 Months)
- Established clear ownership and definitions for priority Service & Operational datasets and critical data elements (e.g., core ITSM practices (Incident/Change/Problem/Request), Assignment Groups, CI mapping, telemetry tags).
- Implemented automated DQ monitoring and scorecards for 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.
#LI-LT1
What’s in it For You?
Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while delivering a seamless experience that is digitally and physically connected.
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Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
Culture: Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
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