Data and Analytics Manager
Novartis
Data Analytics Manager Location Hybrid work in Montréal, QC : Summary Location: Montreal, #LI-Hybrid Novartis is unable to offer relocation support for this role; We are seeking a hands-on Data Analytics Manager who thrives at the intersection of strategy and execution. In this role, you won't just manage projects, you'll actively contribute to their technical delivery. From writing production-grade Python and SQL to deploying pipelines and building dashboards, you will play a direct role in shaping our data products. This position covers all areas of a modern data team, including Data Engineering, Data Governance, Advanced Analytics, Machine Learning / AI, and Dashboarding. The role reports directly into the Data Strategy Lead and will work closely with Business Excellence & Execution cross-functional team. Permanent position About the Role
Lead cross-functional analytics initiatives from planning to delivery, aligning scope, timelines, and priorities with stakeholders.
Translate business requirements into actionable data and analytics solutions across domains.
Provide hands-on support and architectural guidance across data pipelines (ETL/ELT), model development, and reporting solutions.
Collaborate with data engineers to design data models and efficient dataflows in Snowflake and Dataiku.
Partner with analysts and data scientists to drive insight generation, ML model deployment, and automation use cases.
Collaborate with governance stakeholders to enforce data stewardship, lineage, and cataloging using Collibra.
Establish data quality frameworks and documentation standards across the data lifecycle.
Oversee the development and maintenance of Power BI dashboards that provide actionable insights to business leaders.
5+ years of experience in data & analytics.
- Proven ability to manage complex data projects across multiple domains and stakeholders.
- Hands-on experience with platforms like AWS, Snowflake, Collibra, Dataiku, and PowerBI, and proficiency in Python and SQL.
- Strong understanding of data engineering, ML lifecycle, governance principles, and dashboard best practices.
- Technical background (Software Engineering, Computer Science, Information Systems).
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