The Data Architect will shape and lead the data architecture strategy that enables trusted, scalable, secure, and AI-ready data capabilities across the organization. In this role, you will provide technical and architectural leadership across the design, governance, modernization, and implementation of enterprise data solutions and platforms supporting analytics, reporting, machine learning, and AI. You will combine strategic architecture leadership with hands‑on technical expertise, contributing directly to solution design, data platform engineering, and implementation where your expertise can have the greatest impact. You will partner with business and technology stakeholders, architects, and engineers to understand data requirements, define data domains and consumption patterns, and design scalable, secure data solutions that support business outcomes. Define and evolve the segment data architecture vision, principles, standards, and roadmap. Design scalable Data Lake, Data Warehouse, and Lakehouse architectures supporting analytical, operational, and AI workloads. Establish reusable architecture patterns and standards for data integration, metadata management, data governance, data quality, and data products. Lead data modernization, migration, and cloud transformation initiatives, helping teams evolve legacy data environments toward modern cloud-based platforms. Provide hands‑on technical leadership in the design and implementation of data pipelines, integration frameworks, and cloud-native data platforms, including data ingestion, transformation, orchestration, and performance optimization. Design scalable batch, streaming, event-driven, and near real-time data processing solutions based on business and technology needs. Embed data security, governance, privacy,



and regulatory requirements into architecture and solution design in partnership with governance, risk, and security teams. Design AI-ready data architectures that enable machine learning, advanced analytics, and generative AI solutions. Provide technical and architecture leadership across strategic data initiatives, including architecture reviews, technology evaluations, solution trade-offs, and key design decisions. Collaborate with business stakeholders, architects, engineers, and other technology partners to translate business needs into practical, scalable data solutions. Extensive experience architecting, designing, and delivering large-scale data solutions, typically gained through 8+ years of relevant experience or equivalent demonstrated expertise. Hands‑on experience designing or implementing solutions using modern data technologies such as Microsoft Fabric, Azure Data Factory, Azure Synapse Analytics, Databricks, Spark, or comparable platforms. Strong knowledge of enterprise data architecture, data modeling, data integration, and modern Data Lake, Data Warehouse, and Lakehouse patterns. Proficiency in SQL and at least one relevant programming or scripting language, such as Python, PySpark, Java, or Scala, along with working knowledge of SQL and NoSQL database technologies. Experience designing modern data solutions using batch, streaming, and/or event-driven patterns across analytical,



operational, or AI/ML workloads. Strong understanding of enterprise data management practices, including data governance, data lineage, data quality, and master or reference data management. Experience leading cloud-based data platform implementations or modernization initiatives, with the ability to provide architecture direction while remaining engaged in technical solution design and implementation. Strong understanding of data security, privacy, access control, regulatory requirements, and responsible data use. Ability to evaluate technologies, document architecture decisions, lead architecture reviews, and clearly communicate technical options and trade-offs to both technical and non-technical stakeholders. Experience implementing governed data products, domain-oriented data architectures, or data mesh concepts in a large enterprise environment. Experience working in a large, complex, or highly regulated organization, such as financial services, insurance, banking, wealth management, retirement, or another regulated industry. Familiarity with machine learning, MLOps, and data architectures supporting generative AI solutions. Working knowledge of DataOps, CI/CD, automation, platform monitoring, performance optimization, and operational resilience for data platforms. Bachelor's degree in computer science, engineering, information systems, or a related discipline, or equivalent practical experience. We’ll recognize and support you in a flexible setting where well-being and inclusion are more than just words. Additionally, employees also have the opportunity to participate in incentive programs and earn incentive compensation tied to business and individual perfor

📌 Data Modeler (Canada)
🏢 Société Financière Manuvie
📍 Canada