Lead Data Governance & Quality Analyst

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.

Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.

Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s chal