Senior Data Engineer, Retirement Operations

We are seeking an experienced

Senior Data Engineer

to support our North American Retirement organization. The role has significant exposure to Senior Leadership and opportunities to collaborate with business stakeholders and data professionals alike. Your day-to-day will involve partnering with business and technology teams to translate data needs into scalable solutions; designing, building, testing, and monitoring cloud-based data pipelines and models; troubleshooting data quality and production issues; optimizing platform performance and reliability; reviewing code and promoting engineering standards; and supporting analytics, reporting, and machine learning use cases with trusted, well-governed data.
Manulife is a leading international financial services provider, helping people make decisions easier and lives better. Help shape the future you want to see — and discover that better can take you anywhere you want to go.
JOB DESCRIPTION

Key Responsibilities

Collaborate with fellow data engineering colleagues across the organization to deliver secure, reliable, and efficient data engineering solutions
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.
Collaborates with business and technology stakeholders to understand current and future data infrastructure requirements.
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.
Designs, implements, and maintains existing and future data platforms like data warehouses, data lakes, data lakehouses 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.
Utilizes a variety of data interchange formats to ensure data requirements are met and continuously monitors data integrity across the organization.
Deploys machine learning models with existing production systems and workflows, considering compatibility with other systems, data sources, and APIs.
Designs and promotes effective use of data querying APIs to provide easy 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.