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Job Description
Role Summary:
- Leads the IBU Data Operations team, with direct accountability for managing and developing a team of 5-10 direct reports across data integration, architecture, MDM, governance, and data products. The role ensures reliable delivery, operational discipline, performance management, and continuous improvement across IBU and GPD data domains. It works in close partnership with leaders who define data strategy, translating strategic priorities into practical operating plans, delivery execution, and sustained operational outcomes.
How the job will contribute:
People leadership & team development
- Provide direct leadership, coaching, and performance oversight for a team of 5-10 direct reports across IBU Data Operations, spanning data integration, architecture, MDM, governance, and data product delivery.
- Set clear team priorities, role expectations, and individual development plans; build a high-performance culture defined by accountability, psychological safety, and continuous learning.
- Actively develop capability across the team, identifying skill gaps, supporting career growth, and ensuring the team is equipped for evolving business and technology demands.
- Foster effective collaboration across IBU counterparts, ICC pods, and enterprise teams, ensuring cohesion and alignment across delivery nodes.
Operating model & delivery governance
- Oversee the end-to-end operating model for IBU data operations, ensuring teams are organized, resourced, and coordinated to deliver reliably against agreed priorities across all data domains.
- Translate priorities set by data strategy, business, and DD&T leaders into practical delivery plans, team objectives, sprint-level execution, and operational routines; ensuring strategic intent becomes measurable delivery.
- Manage cross-team dependencies across ICC pods, IBU counterparts, enterprise teams, LOCs, and business functions; ensure capacity, sequencing, and trade-offs are transparent, well-communicated, and aligned to business priorities.
- Maintain delivery governance frameworks and rituals, including planning cycles, status reporting, and operational reviews that provide visibility and accountability across the data operations portfolio.
BAU data integration ownership
- Own end-to-end operational accountability for the BAU EDB data integration pod, including reliable and performant pipeline operations, ingestion flows, platform connectivity, and service continuity across IBU markets.
- Serve as the senior escalation point for integration incidents, data outages, and change requests, ensuring timely resolution, minimal downstream disruption, and clear stakeholder communication.
- Proactively identify and resolve systemic issues in integration infrastructure, pipeline reliability, and platform performance before they impact business consumers.
Data product & pipeline lifecycle
- Oversee the full lifecycle of IBU data products and pipelines, from design and build through transition to operations, continuous improvement, and ongoing service management.
- Ensure data products and pipelines meet AI-readiness, data quality, governance, and master data standards in line with IBU's data excellence principles and enterprise expectations, working in close collaboration with IBU Data Architecture and Data Product Leads.
- Partners with data product owners, architects, and engineers to ensure operational requirements and serviceability constraints are embedded from the design stage.
Operational performance & continuous improvement
- Maintain and continuously improve operational governance, performance metrics, and best practices across the IBU data ecosystem; driving improvements in quality, speed, scalability, reliability, and accountability.
- Establish metrics, SLAs, and review processes that surface issues early, enable proactive resolution, and drive a culture of continuous improvement across data operations. Identify operational inefficiencies and drive structured improvement initiatives, including automation, tooling upgrades, and process redesign in close partnership with IBU Data Excellence
Expected Skills:
Leadership & people management
- Proven track record of leading, developing, and managing direct reports in a complex, matrixed environment; including setting direction, managing performance, building capability, and creating an accountable team culture.
- Experienced in operating as a leader in ambiguous, fast-paced environments; able to make sound decisions, manage risk, and escalate appropriately when required.
- Able to translate strategy and business priorities set by others into practical operating plans, delivery roadmaps, team objectives, and measurable execution outcomes.
Stakeholder engagement
- Highly effective in stakeholder engagement and cross-functional collaboration; able to act as a trusted operational partner to DD&T, enterprise teams, LOCs, business functions, and strategy-setting leaders.
- Strong communicator in English (oral and written), able to translate complex technical topics into clear operational implications, risks, decisions, and actions for diverse audiences.
Technical expertise
- 10+ years of relevant technical and operational experience, including systems analysis, solution design, implementation, data operations, and lifecycle management of enterprise data products.
- Deep expertise in data platforms, integration patterns, and data pipeline architecture; including hands-on familiarity with statistical programming languages (R, Python) and database query languages (SQL).
- Strong knowledge of data integration technologies, ETL/ELT frameworks, cloud data platforms, and enterprise integration patterns.
- Familiarity with data governance frameworks, MDM principles, data quality tooling, and metadata management practices.
Domain knowledge
- Deep knowledge of the pharmaceutical or life sciences business model, including regulatory expectations, compliance requirements, and the responsible use of AI-ready data assets.
- Understanding of healthcare data standards, market heterogeneity challenges, and the operational complexity of managing data across multi-market environments.