Data Engineer
- Toronto, ON, Canada
$60 to $80 CAD / hour · Contract
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Job details
- Pay
- $60 to $80 CAD / hour
- Job type
- Contract
- Apply by
- Experience
- 3+ years of Data Engineering experience
- Posting language
- English
- Posted
Original job description
Required Skills & Experience
- Strong Python development experience.
- Advanced SQL skills.
- 3+ years of Data Engineering experience
- Hands-on experience developing and migrating data pipelines.
- Proven experience migrating data/workloads from on-premises environments to AWS.
- Strong hands-on experience working within the AWS ecosystem.
- Experience integrating and transforming data from multiple sources.
- Strong communication and stakeholder-management skills.
- Ability to operate in a consultative capacity, working directly with the business. Experience working within a large enterprise environment.
Nice to Have Skills & Experience
Banking / FS experience
Commercial Banking
Job Description
We are seeking a Data Engineer to support a large enterprise transformation focused on building data pipelines, integrating data, and migrating data workloads from on-premises environments to AWS. This person should be technically strong in Python and SQL, have hands-on AWS migration experience, and be comfortable partnering directly with business stakeholders. Experience with Machine Learning and MLOps is a strong plus, particularly supporting models through integration and production environments. The scope of work will be contributing to Commercial Banking's Underwriting AI enablement/automation vision
Key Responsibilities
- Design, develop, and maintain data pipelines.
- Migrate existing data pipelines and workloads from on-premises infrastructure to AWS.
- Gather, integrate, transform, and prepare data from multiple sources.
- Use Python and SQL extensively for data development and manipulation.
- Support integration of data into existing enterprise platforms and applications.
- Partner with business and technical stakeholders to understand requirements and translate them into technical solutions.
- Support ML/MLOps integration into production environments where applicable.
- Work within an enterprise environment supporting business-critical applications and workflows.
- Communicate technical concepts clearly to both technical and non-technical stakeholders.
Artificial intelligence tools are used to assist in screening and assessing applicants for this position.
Vacancy: This posting is for a currently vacant role, and the successful candidate will be hired into an existing open position.
60-80/hr
More about this job
Responsibilities
Design, develop, and maintain data pipelines, including migrating existing pipelines and workloads from on-premises infrastructure to AWS. Integrate and prepare data from multiple sources, support enterprise platform and ML/MLOps integrations, and collaborate with business and technical stakeholders to deliver solutions for Commercial Banking underwriting automation.
Requirements
Requires strong Python and advanced SQL skills, at least three years of data engineering experience, hands-on pipeline development and migration experience, and practical experience migrating workloads from on-premises environments to AWS. Candidates should also have experience integrating data from multiple sources, working in large enterprises, and communicating effectively with business stakeholders; banking or financial services experience and ML/MLOps are preferred.
Skills
- Python
- Advanced SQL
- Data Engineering
- Data Pipeline Development
- Data Migration
- AWS
- Data Integration
- Data Transformation
- Stakeholder Management
- Business Communication
- Machine Learning
- MLOps
- Enterprise Data Platforms
- Commercial Banking
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Finance & Accounting
- Consulting
Keywords
- Python
- SQL
- Data Engineering
- Data Pipelines
- AWS
- Cloud Migration
- On-Premises Infrastructure
- Data Workloads
- Data Integration
- Data Transformation
- Enterprise Platforms
- Machine Learning
- MLOps
- Production Environments
- Commercial Banking
- Underwriting
- AI Enablement
- Automation
- Stakeholder Management
- Business-Critical Applications