Hybrid Associate Data Engineer

at Manulife

• Job title: Associate Data Engineer
• Job type: Full Time
• Experience level: Junior, Mid level
• Salary: CA$60,900 - CA$113,100 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Azure, Cloud, HDFS, MapReduce, NoSQL, PySpark, Scala
• Location requirements: Hybrid, Toronto, Canada

About the role

• Associate Data Engineer designing, building, and maintaining data solutions at Manulife. Collaborating with Data Scientists and Engineers to enhance data workflows and model deployment.

Responsibilities

• Designs, builds, and maintains reliable, efficient and scalable data infrastructure for data collection, storage, transformation, and analysis.
• Implements data orchestration pipelines, data sourcing, cleansing, augmentation, and quality control processes.
• Works with business and technology collaborators to grasp current and future data infrastructure needs.
• Designs, builds and maintains scalable data solutions including data pipelines, data models, and applications for efficient and reliable data workflow; including those specifically tailored for machine learning workflows.
• Builds, implements, and upholds current and upcoming data platforms such as data warehouses, repositories for structured and unstructured data.
• Collaborates with Data Scientists and Engineers to create features and pre-process data for ML models and move data analysis models into production.
• Designs and develops analytical tools, algorithms, data landscape modernization roadmaps, and programs to support Data Engineering activities like writing scripts and automating tasks.
• Applies a variety of data interchange formats to ensure data requirements are met and continuously monitors data integrity across the organization.
• Integrates machine learning algorithms into current production systems and workflows, taking into account compatibility with other systems, data sources, and APIs.
• Builds and advocates for efficient utilization of data querying APIs to ensure seamless access to organizational data sources.
• Evaluates, integrates, and manages tools and frameworks within the data engineering ecosystem, ensuring compatibility and efficiency in model development and deployment.
• Designs and promotes data versioning and lineage tracking, including transparency and traceability for data used in ML model training and inference.

Requirements

• Knowledge of database systems, data lakes, and NoSQL databases
• Knowledge of data warehouse concepts and architectures (e.g., Synapse)
• Familiarity with data quality and data modelling tools
• Proficiency in using version control systems like Git for managing codebase
• Experience with Cloud native data services such as PySpark, Scala, Azure Data Factory and Databricks
• Practical experience with big data processing frameworks and techniques such as HDFS, MapReduce, Storage formats (Avro, Parquet), Stream processing
• Experience with integrating to back-end/legacy environments
• Knowledge of AI model deployment in production environments
• Experience handling real-time data for AI Applications
• Ability to build and deploy Data Ops and ML Ops Pipelines in Cloud-native environments

Benefits

• health, dental, mental health, vision insurance
• short- and long-term disability
• life and AD&D insurance coverage
• adoption/surrogacy and wellness benefits
• employee/family assistance plans
• retirement savings plans (including pension and employer matching contributions)
• customizable benefits
• paid time off including holidays, vacation, personal days, sick days
• statutory leaves of absence