Job Description

Sr. Data Architect to work on a Call Center as a Service modernization project for a large insurance client - 4664

Duration: 1 year (possibility of extension)

Location: Remote (preferably Toronto or Waterloo)

Reporting to the Director, Enterprise Data Architecture, you will be responsible for contributing to enterprise date architecture initiatives and programs that support business priorities and technical roadmap. You will design and create technical data models and schemas for advanced data architectures such as Data Fabric & Mesh on cloud. You will work closely with the team of Enterprise Architects (across domains and platforms) and Data and Analytics Team who interact closely with the business in understanding business needs and architecting solution options.

Must Have Skills:

• 7+ years of experience in AWS Architecture and Data Architecture
• Experience with modern databases (RDBMS, NoSQL, Graph, etc.)
• Knowledge of Master Data Management (MDM) through its initial setup, implementation, and execution, aligning to business use cases.
• Experience in large financial organization or a large regulatory organization

• Call-center experience
• Experience with AI (Artificial Intelligence) and ML (Machine Learning) initiatives on cloud for voice and text data

• Creates blueprints for data management systems to integrate, protect and maintain data patterns. Design concepts to make the flow of data in company as efficient as possible
• Develop data models and schema designs to support platform / projects requirement. Supervise other architects use of tools and technologies.
• Scope the types of data management frameworks to be used.
• Implement a data catalog of enterprise data assets and characteristics
• Develop a reference architecture that includes data domain specs
• Specify data access methods and architects data services
• Ensure compliance with data quality and protection policies
• Support Data and Analytics center in translating strategic requirements into usable enterprise data architectures.
• Develop a metamodel management and repository strategy to manage all enterprise data architecture artifacts.
• Identify data workload drivers for the selection between cloud and on premise, based on factors such as latency, consumption patterns and others for systems of record, engagement, and insight.
• Ensure the enterprise information architecture alignment to the overall enterprise architecture strategies.
• Develop and maintain effective and efficient information management, business intelligence, and data analytics ownership awareness and training programs.

S.i. Systems