Azure AI/Data Architect
Manulife View all jobs
- Toronto, ON
- Permanent
- Full-time
- Design and implement data integration solutions to facilitate seamless data flow between systems.
- Architect and oversee data ingestion, transformation, and orchestration using ADF/Synapse pipelines, Databricks Workflows, and event-based patterns
- Establish data modeling standards (dimensional/Kimball, Data Vault, lakehouse models) and semantic layers for BI/ML consumption
- Implement governance: data catalogs/lineage, DQ frameworks, security, RBAC/ABAC, and compliance alignment (e.g., Purview, Unity Catalog)
- Design scalable ML platforms: feature stores, model training/serving, experiment tracking (MLflow), and CI/CD for ML (MLOps)
- Optimize performance and cost: cluster sizing, autoscaling, storage layout (partitioning/Z-ordering), caching, and query tuning (Spark SQL/PySpark)
- Define data contracts/APIs and integration patterns for cross-domain interoperability and application consumption
- Partner with engineering, data science, and business collaborators to translate requirements into technical roadmaps and reference architectures
- Lead POCs, platform modernization, and migration efforts (on-prem to Azure, batch to streaming, BI to lakehouse)
- Build standards, templates, and guidelines; mentor teams and conduct architecture reviews
- Maintain existing integration frameworks and guide each squad
- Evaluate emerging data technologies and tools to improve data architecture and processes.
- Monitor and optimize the performance of data systems to ensure efficient data processing
- Develops, builds, and maintains reliable, efficient and expandable data systems for data collection, storage, transformation, and analysis.
- Implements data orchestration pipelines, data sourcing, cleansing, augmentation, and quality control processes.
- Collaborates alongside business and technology partners to gather a clear understanding of current and future data infrastructure requirements.
- 7+ years in data architecture/engineering; 3+ years hands-on with Databricks and Delta Lake
- Strong Azure expertise: Azure Databricks, Data Factory, Synapse, Event Hubs, Storage (ADLS Gen2), Key Vault, Azure DevOps
- Proficiency in Spark (Core/SQL), PySpark, and SQL; experience with performance tuning and large-scale data processing
- Experience designing Medallion Architecture and implementing data governance (Purview/Unity Catalog, DQ frameworks)
- MLOps/AI platform knowledge: MLflow, feature stores, model lifecycle, and deployment patterns
- Understanding of security, compliance, and data privacy; implementing least-privilege, encryption, and auditability
- CI/CD for data and ML, Infrastructure-as-Code (Bicep/Terraform), and automation
- Good communication, managing relationships, and technical leadership skills
- Bachelor’s Degree: Usually in computer science, information technology, information systems, or a related field.
- Advanced Degrees: A Master’s degree in data science, computer science, or business administration can be advantageous and preferred for senior roles.
- Several years (typically 5-10) of experience in data management, database design, data warehousing, or a similar field.
- Experience with ETL tools and processes (e.g., Databricks, Informatica).
- Knowledge of data integration patterns and standard methodologies.
- Hands on experiences with technologies like Spark, Hive, Python, Terraform .
- Experience with cloud platforms such as Azure and AWS.
- Experience with streaming architectures: Kafka/Event Hubs, Structured Streaming, CDC
- Knowledge of Power BI/semantic models and cross-platform integrations (Synapse SQL, Fabric, Snowflake)
- Multi-cloud awareness (AWS/GCP) and interoperability patterns
- Financial services or regulated industry experience (e.g., BCBS 239, SOX, HIPAA)
- We’ll empower you to learn and grow the career you want.
- We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.
- As part of our global team, we’ll support you in shaping the future you want to see.