Data Operations Manager, Digital Finance

• Bachelor's or master's degree in computer science, Engineering, Data Science, or a related field.
• Typically 2-3 years of experience in product operations, ideally within data platforms and Agile environments.
• Proven ability to manage timelines, resources, and deliverables across complex data product lifecycles.
• Strong stakeholder engagement skills, translating business needs into actionable operations solution requirements.
• Experience coordinating cross-functional teams and managing scopes with stakeholders and technical partners.
• Familiarity with finance domain processes and systems; understanding of financial data flows is a plus.
• Solid grasp of modern data architectures including Data Lake, Lakehouse, and Data Mesh.
• Experience with T-SQL, data warehouses (e.g., Snowflake, Databricks), and ETL pipelines; understanding of BI tools and data visualization concepts is a plus.
• Foundational GenAI and AI-agent fluency, including safe use of approved tools, instruction quality, human review, data privacy and escalation of unexpected outputs.

• Experience working in multi-functional teams across various organizational levels and business domains.
• Familiarity with data management capabilities such as data catalogues, semantic layers, master or reference data and data contracts.

Step into a pivotal role where data meets strategy. As the Data Operations Manager in Digital Finance, you'll be at the heart of transforming financial data into actionable insights. You'll lead the operational lifecycle of finance data products, ensuring accuracy, scalability, and seamless collaboration across technical and business teams. This is your opportunity to impact how Novartis leverages data to drive smarter financial decisions and deliver real impact across the organisation.

• Serve as the primary liaison across various stakeholders: Finance, Digital teams, and DDIT to align and operate business data product operations.
• Lead and support end-to-end operational activities of enterprise Finance data lake, lakehouse and warehouse products.
• Build knowledge of Finance processes, source systems, data flows and platform capabilities, taking on increasing ownership of defined operational areas.
• Monitor scheduled data loads, pipelines, interfaces, completeness, performance and services across monthly close, planning, forecasting and other critical Finance cycles.
• Execute and progressively automate reconciliations, control checks and data-quality rules covering accuracy, completeness, timeliness, consistency and integrity, with audit-ready evidence.
• Triage incidents and data-quality exceptions; capture business impact, perform first-line diagnosis, coordinate resolution and maintain transparent status communication.
• Maintain SOPs, support models, escalation paths, load calendars, dependency maps, known-error records and recovery procedures.
• Manage and support governed master data access workflows, security groups, data-sharing reviews, retention, archival and deletion in line with privacy, security and records-management requirements.
• Support AI and agent-ready data operations by validating that approved data sources, semantic context and metadata are current, traceable and accessible within defined entitlements.
• Collaborate cross-product operational changes through proactive communication across Digital Finance product teams and engage respective stakeholders.
• Engage Finance users, Product teams through clear communications, knowledge articles and training, and channel recurring feedback into the backlog.

Critical Thinking, Data Visualization, Employee Engagement, Insight Generation, Managing Ambiguity, Market Understanding, Process Optimization