1. Role purpose  The Senior Configuration Management Lead is the senior hands-on technical lead for the operation and assurance of LSEG's enterprise CMDB. The role converts configuration management standards and data-model requirements into reliable operational capability, ensuring that configuration data is complete, correct, current, controlled and usable across on-premises, cloud and hybrid technology estates.  The role leads CMDB engineering, source onboarding, reconciliation, data-quality automation and control execution. It works across ServiceNow, authoritative technology sources and the enterprise data platform to create trusted, reusable configuration data products, while applying AI and automation with clear human validation, security controls and auditable evidence.  Core outcome: move SCM from manual detection and remediation toward source-driven, measurable and increasingly automated control operation, without weakening human accountability or production safeguards. 2. Strategic context  • LSEG is moving toward trusted, governed and interoperable data foundations that support analytics, AI, automation and reusable data products.  • The SCM operating model therefore requires more than traditional CMDB administration. It requires engineering discipline across ingestion, identification, reconciliation, quality controls, exception management, evidence and data consumption.  • This role is responsible for the operate-and-assure layer for configuration data. Architecture and standards define how data should be structured; the Senior Configuration Management Lead engineers, runs and improves the mechanisms that make the data dependable in practice.  • AI is used as a force multiplier for analysis, documentation, anomaly detection, evidence preparation and remediation prioritisation. Material decisions and production changes remain subject to human review and established approval controls.  3. Key accountabilities  CMDB engineering and configuration management  Act as the senior SCM technical lead: Provide authoritative operational guidance for CMDB enhancements, integrations, data-source onboarding, identification and reconciliation rules, relationship handling and lifecycle controls.  Lead capability delivery: Translate SCM requirements into implementable changes and lead delivery from definition through design input, test assurance, deployment readiness, adoption and measurable outcome.  Operate and assure the CMDB: Ensure that approved configuration management processes, control requirements and data standards are applied consistently across in-scope CI classes and technology domains.  Define operational scope and exceptions: Maintain clear operational scope, class coverage, source responsibilities and governed exceptions, increasing material risks through the Process Owner and relevant governance forums.  Data quality, reconciliation and remediation  Engineer data quality at source: Work with infrastructure, cloud, network, database, storage and application teams to prevent poor-quality data entering or updating the CMDB, using quality gates and clearly defined source ownership.  Automate reconciliation: Design and operate repeatable comparisons between ServiceNow and authoritative inventories, using identification and reconciliation controls to prevent duplicates, conflicting updates and incorrect classification.  Drive remediation to closure: Prioritise configuration data defects by operational and control risk, establish owners and target outcomes, and ensure exceptions are supervised through to evidence-based closure.  Improve relationship integrity: Strengthen application-to-service and service-to-infrastructure relationships so configuration data supports impact analysis, incident, change, resilience and audit use cases.  AI and automation-enabled SCM  Develop automation use cases: Identify and shape high-value automation for data validation, duplicate detection, stale or orphan identification, source-to-CMDB comparison, evidence production and remediation workflow.  Apply AI responsibly: Use Copilot and approved AI capabilities to accelerate analysis, documentation, rule development and exception triage, while validating outputs and retaining human accountability for decisions and changes.  Embed controls as code: Help translate SCM policies, control criteria and data-quality rules into version-controlled, testable and repeatable logic that generates traceable evidence.  Measure automation outcomes: Track reduction in manual effort, improved detection coverage, faster remediation and improved control reliability rather than treating automation delivery as an end in itself.  Data platform and data product integration  Define trusted SCM data products: Partner with data platform engineering to define curated configuration datasets, quality rules, ownership, lineage, refresh expectations and access patterns for operational and analytical consumption.  Support strategic ingestion patterns: Enable source-driven integrations through governed APIs, events, CDC or batch patterns, ensuring ServiceNow remains appropriately controlled while configuration data can be reused beyond the platform.  Provide semantic and metadata input: Supply SCM domain knowledge to shared data definitions, class semantics, mandatory ributes, relationships and reusable contracts that support analytics, AI and agentic use cases.  Close the feedback loop: Ensure quality findings and execution outcomes from data-platform and automation workflows are routed back into SCM governance, remediation and evidence processes.  Controls, audit and operational assurance  Operate SCM controls: Support the operational effectiveness of ASLM 1.2 and related controls through measurable KCI/KDE outputs, exception governance and sustained evidence generation.  Maintain audit-ready evidence: Ensure that data-quality checks, reconciliations, approvals, exceptions and remediation outcomes are repeatable, traceable and accessible for control owners, Risk, Internal Audit and external assurance.  Lead test assurance: Define and supervise appropriate testing for CMDB changes, integrations, reconciliation rules and automation, including expected results, defect resolution and production-readiness evidence.  Resolve systemic failures: Investigate recurring process and data failures, identify root causes and drive corrective action across teams rather than relying on repeated manual cleanup.  Technical leadership and continual improvement  Lead through influence: Coordinate virtual squads across SCM, ServiceNow, architecture, data engineering and infrastructure domains, without requiring direct line-management authority.  Establish team capability: Mentor SCM practitioners in CMDB engineering, data quality, automation, testing, evidence and modern engineering practices.  Maintain operational documentation: Keep process, procedure, work instruction, decision and control documentation current, usable and aligned to implemented capability.  Drive measurable improvement: Use metrics and structured improvement cycles to increase coverage, accuracy, freshness, relationship integrity, automation and remediation effectiveness.  4. Key relationships  • Service Configuration Management Process Owner and SCM practitioners.  • ITSM Architecture, CSDM and ServiceNow platform engineering.  • Asset Management, Service Operations, Service Transition and Service Performance teams.  • Cloud, Network, Server, Database, Storage, application and service-owner communities.  • Data platform, data engineering, automation and AI capability teams.  • Technology Risk, control owners, Internal Audit, external assurance and regulatory collaborators.  5. Skills and experience  Crucial  • Significant hands-on experience in Service Configuration Management and enterprise CMDB operation, with evidence of leading sophisticated technical outcomes.  • Deep practical ServiceNow CMDB knowledge, including CI classes, relationships, identification and reconciliation, integrations, CMDB Health and data certification. Discovery and CSDM experience is strongly preferred.  • Demonstrable experience improving configuration data quality through source reconciliation, de-duplication, lifecycle controls, classification, relationship validation and structured remediation.  • Experience translating process, control or data-quality requirements into implementable technical requirements, test scenarios and operational procedures.  • Practical automation capability using one or more of Python, scripting, APIs, data pipelines, Git-based workflows, CI/CD or comparable engineering tooling.  • Practical experience using AI or Copilot responsibly for analysis, engineering, documentation or quality activities, including validation of generated outputs.  • Ability to work with data-platform concepts such as ingestion, curated datasets, lineage, metadata, quality rules, APIs, events and historical data consumption.  • Strong controls and evidence rigor, with the ability to explain how a control operates and demonstrate repeatable, auditable outcomes.  • Clear customer communication and the ability to lead technical decisions and delivery through influence across multiple teams.  Desirable  • Experience with Snowflake, CDC, lakehouse or unified data-platform patterns.  • Knowledge of configuration-data semantic models, data dictionaries or metadata-management practices.  • Experience with controls as code, policy as code, automated evidence or agent-assisted operations.  • ITIL certification and knowledge of COBIT, DORA or supervised financial-services control environments.  • Experience mentoring practitioners and leading virtual technical squads.  We're proud to have been recognised as a Great Place to Work® in India ‘25 Career Stage: Manager London Stock Exchange Group (LSEG) Information: Join us and be part of a team that values innovation, quality, and continuous improvement. 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