At a Glance
- Tasks: Define and implement a quality framework for enterprise knowledge graphs.
- Company: Join NTT DATA, a leader in AI and digital infrastructure.
- Benefits: Flexible hybrid work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative culture with strong focus on innovation and career development.
- Why this job: Make a real impact on data quality and governance in a dynamic environment.
- Qualifications: Experience in data quality frameworks and graph analytics required.
Req ID:376760NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. We are currently seeking a Knowledge Quality Consultant to join our team in London, London, City of (GB-LND), United Kingdom (GB).Knowledge Quality LeadExperience: 8-12 years (5+ years in data quality, analytics, or knowledge graph environments)Role OverviewWe are seeking a Knowledge Quality Lead to define and operationalize a multi-dimensional quality framework for enterprise knowledge graphs. This role will ensure high standards across completeness, correctness, provenance, and currency of data, while driving governance, audits, and remediation at scale.The ideal candidate will combine expertise in data quality frameworks, graph analytics, and audit methodologies with the ability to lead quality initiatives across teams.Key ResponsibilitiesQuality Framework & MetricsDefine and implement a multi-dimensional quality frameworkEstablish metrics for:CompletenessCorrectnessBinding correctnessProvenanceCurrencyContinuously refine quality benchmarks aligned with business needsQuality Audits & MonitoringConduct periodic data quality audits across the knowledge graphLeverage graph analytics and Cypher queries for validation and insightsIdentify gaps, anomalies, and inconsistencies in graph dataReporting & Stakeholder VisibilityDesign and publish quality dashboards and scorecards for stakeholdersProvide actionable insights on data quality trends and risksDrive transparency and accountability across teamsRemediation & GovernanceOwn and prioritize the data quality remediation backlogDefine and enforce remediation SLAsCollaborate with engineering and domain teams to resolve quality issuesEstablish governance processes for ongoing quality assuranceCollaborationPartner with data engineering, ontology, and AI teamsAlign quality standards with enterprise data governance frameworksPromote best practices in data quality management and validationRequired SkillsData Quality Frameworks (profiling, validation, governance)Graph AnalyticsCypher (querying and validation in graph environments)Audit Methodology (data audits, controls, compliance)Preferred SkillsExperience with knowledge graphs / Neo4jData governance and metadata managementDashboarding tools (Power BI, Tableau, etc.)Exposure to AI/ML data validation practicesJob ExpectationsEstablish and drive enterprise-grade data quality standardsEnsure continuous monitoring and improvement of knowledge graph qualityDeliver actionable insights and measurable improvementsEnable trust and reliability in AI and downstream applicationsSummaryThis role is critical in ensuring the trust, reliability, and usability of enterprise knowledge graphs, enabling high-quality data for analytics and AI use cases through structured quality frameworks and strong governance.About NTT DATANTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com and @talent.NTT DATA endeavors to make accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at . This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here.
Quality Advisor (Hybrid) in London employer: NTT DATA
At NTT DATA, we pride ourselves on being an excellent employer that fosters a culture of innovation and collaboration. Our commitment to employee growth is evident through continuous learning opportunities and a supportive environment that encourages creativity in the rapidly evolving field of AI/ML security. Located in a vibrant tech hub, we offer competitive benefits and a dynamic workplace where your contributions directly impact the future of secure AI systems.
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We think you need these skills to ace Quality Advisor (Hybrid) in London
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Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
