Senior Data Engineer

Requirements

• Solid understanding of RESTful APIs with FastAPI (authentication, rate limiting, pagination, error handling)
• Experience in deploying and operating containerized applications on AKS (Azure Kubernetes Service)
• Good understanding of Agentic AI frameworks like LangChain/AutoGen..
• Exposure to A2A (Agent to Agent) implementation & Agent scaling
• When you join our team:
• 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.
• The role being advertised is an existing vacancy.
• About Manulife and John Hancock
• Manulife Financial Corporation is a leading international financial services provid

Benefits

Additional Information

The Data Engineering Lead is a hands-on technical expert who builds scalable data platforms, data pipelines, automated tools, and reusable data products that expand the organization's Data & AI capabilities. This role also partners closely with Sales, Marketing, Product, and other business stakeholders to translate opportunities into clear data solutions and to ensure what we build drives measurable business impact and growth. Office location: Toronto - Canada Work arrangement: Hybrid - 3 days in office, 2 days from home; Remote working option is not available . Position Responsibilities: 1) Build scalable data platforms, products, and automation Design and build scalable data pipelines, automated data tools, and reusable data products on modern cloud platforms such as Azure Databricks to accelerate AI and analytics value delivery. Automate, modernize, and improve data pipelines and engineering processes to increase efficiency, reliability, and scalability. Deliver projects across data ingestion, engineering, visualization, and decision-making. 2) Own the data domain and strengthen engineering quality Serve as a data-domain subject matter expert and trusted technical advisor. Build a strong understanding of systems and the data environment by researching, exploring, and testing new data architecture, design ideas, and technologies. Collaborate with internal and external experts to improve data quality, platform reliability, access, and usability. Use technical documentation to develop a holistic understanding of the data domain. 3) Enable analytics through strong data foundations and business context Work with data scientists to turn business requests into high-value data products and capabilities. Partner with business teams and data scientists to build, improve, and deliver trusted, analysis-ready data. Help develop conceptual/logical data models, curated data layers, or semantic layers with clear business context. 4) Partner with the business (Sales/Marketing/Product) and communicate clearly Work with Sales, Marketing, Product, and other teams to understand needs and identify where data and analytics can support growth. Turn business insights into clear technical requirements focused on measurable impact. Explain technical ideas in plain language and confidently present solutions to non-technical audiences. Act as a connector between technical and business teams to keep goals, requirements, and delivery aligned. 5) Grow data and analytics capability across the organization Help business partners understand and use data and automation effectively. Identify and develop "data champions" to increase profitable use of data. Learn key business concepts from partners and enable creative, effective use of data technologies. Required qualifications: Bachelor's degree (or higher) in computer science or quantitative field (e.g., Mathematics, Physics, Engineering). Technical Requirements m ust have: Advanced experience with databases, data architecture, and modern data engineering practices. Strong programming skills in Python, SQL, Spark. Experience building automated data pipelines, orchestration workflows, and data transformation solutions, ideally in Azure Databricks or similar cloud data platforms. Experience with cloud data technologies-especially Azure, including Azure Databricks-and Data as a Service (DaaS). Demonstrated experience leveraging AI/Generative AI tools (e.g., GitHub Copilot, LLM-based assistants, automation frameworks) to accelerate development, improve code quality, and enhance productivity across the software delivery lifecycle Experience with analytics and visualization tools (e.g. PowerBI) is a plus. Business Partnership & Communication Skills Ability to collaborate with Sales, Marketing, and other customer-facing stakeholders. Be able to capture business pain points, showcase prototypes and iterate on solutions Strong communication and storytelling skills; able to simplify complex concepts. Comfortable facilitating sessions, presenting to groups, and engaging partners. Consultative mindset with the ability to link technical work to business outcomes.

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