Manulife

Financial Services

DataEngineer

$113–113k Toronto, Ontario, Canada FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Data Engineer at Manulife. Skills: Data engineering, Data architecture, Data pipelines, Cloud platforms. Design data infrastructure. Build data infrastructure”

Industry & Context.

Financial Services
Problems you'll solve

Problem-solving; Troubleshoot data pipelines

What They're Looking For.

Must Have

Bachelor's or master's degree, 10+ years of experience, Advanced SQL expertise, ETL/ELT pipeline design, Azure data ecosystem experience, PySpark, Python, SQL proficiency, Big data frameworks experience, Data visualization tools knowledge, Data ingestion pipelines experience, Data modeling understanding, Data warehouse architecture understanding, Data lake architecture understanding, Cloud platforms experience, Containerization tools experience, Orchestration tools experience, Version control systems proficiency, Cloud security best practices understanding

Nice to Have

Analytical thinking, Problem-solving abilities, Ability to work independently, Manage complex requirements, Excellent communication skills, Passion for learning

What You'll Do.

Design data infrastructure

Build data infrastructure

Maintain data infrastructure

Develop data pipelines

Manage data pipelines

Implement data orchestration

Build ETL/ELT pipelines

Maintain ETL/ELT pipelines

Contribute to data platform modernization

Enhance reporting platforms

Support reporting platforms

Partner with business stakeholders

Understand data needs

Understand analytical requirements

Translate business problems

Communicate analytical findings

Support advanced analytics

Develop reusable frameworks

Develop automation scripts

Develop analytical tools

Build containerized data solutions

Build analytics platforms

Evaluate emerging technologies

Recommend innovative solutions

Monitor data pipelines

Optimize data pipelines

Troubleshoot data pipelines

Ensure data integrity

Ensure data governance

Document data sources

Validate data sources

Support development lifecycle

Contribute to planning

Collaborate across teams

Drive continuous improvement

Contribute to team success

How You'll Work.

Team & Collaboration

Cross-functional teams; Team members; Stakeholders

Communication Scope

Communicate findings; Technical audiences; Business audiences

Process & Methodology

Roadmap planning

Full Job Description

Join our Group Benefits Product Data Analytics team—a diverse and high-performing group dedicated to delivering accurate data, insightful visualizations, and advanced analytics that drive business decisions and improve customer outcomes. We are seeking a Data Engineer to play a key role in designing and building scalable data solutions that power business intelligence and analytics initiatives. This position spans the full data lifecycle, including data sourcing, transformation, storage, quality, and lineage, while enabling modern data platforms and capabilities. **Position Responsibilities:** **Data Engineering & Architecture** * Design, build, and maintain scalable and efficient data infrastructure for data ingestion, transformation, storage, and analysis * Develop and manage end-to-end data pipelines with a focus on data quality, reliability, and lineage * Implement data orchestration processes, including sourcing, cleansing, enrichment, and validation * Build and maintain robust ETL/ELT pipelines with strong fault tolerance and continuous integration **Data Platform & Solutions Development** * Design and develop data models, pipelines, and applications to enable efficient data workflows * Integrate data from enterprise systems, data lakes, and other internal/external sources * Contribute to data platform modernization initiatives and roadmap planning * Enhance and support enterprise data and reporting platforms **Business & Analytics Collaboration** * Partner with business stakeholders to understand data needs, KPIs, and analytical requirements * Translate business problems into data and analytics solutions, including prototypes and mock-ups * Deliver insights and communicate analytical findings to non-technical audiences * Support advanced analytics use cases, including descriptive and prescriptive analytics **Advanced Data & Innovation** * Develop reusable frameworks, automation scripts, and analytical tools * Build containerized data solutions and analytics p

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