Sr. Data Engineer to design, develop, and maintain data pipelines for AI/ML Applications for ou[...]
4 weeks ago
Location: Downtown Toronto (2 Days in Office)
The Senior Data Engineer will be responsible for designing, developing, and maintaining data pipelines for AI/ML applications. The successful incumbent will collaborate closely with AI Engineers to ensure seamless integration of data-driven solutions, manage ML Ops, and contribute to the development of dashboards and APIs. The incumbent will be an SME in data acquisition, pipeline development, and cloud technologies essential for transforming data into actionable insights.
Must Have Skills:
- 5+ years of experience in a data engineering or software engineering role.
- Proficiency in Python and SQL for data manipulation, extraction, and analysis.
- Experience with both SQL and NoSQL databases, with the ability to design and optimize database schemas.
- Experience in MLOps (Machine Learning) and familiarity with tools like AWS Sagemaker, MLFlow, etc.
- Cloud platforms and services such as AWS, Azure, GCP.
- Knowledge of capital markets.
What you will do:
- Problem-Solving: Approach technical challenges with an entrepreneurial mindset, unblocking the team for faster prototyping and experimentation of data solutions.
- Strive for Efficiency: Test new tools and technologies to generate ideas around AI/ML solutions, enhancing the team’s capability for faster experimentation.
- Data Engineering Expertise: Design, build, and maintain scalable data pipelines that are robust, reliable, and efficient.
- ETL Processes: Implement ETL processes to acquire, transform, and load data from various sources into data storage solutions.
- Scalability: Ensure data pipelines are scalable to handle large volumes of data and perform efficiently under varying workloads.
Data Acquisition and Cleaning:
- Data Integration: Integrate data from diverse sources (SQL, NoSQL, APIs, flat files, etc.) while maintaining data integrity and quality.
- Data Cleansing: Clean and preprocess data to address inconsistencies, missing values, and outliers, ensuring data readiness for analysis and modeling.
- Automated Data Processing: Utilize tools and techniques for automating data acquisition and cleaning processes to streamline workflows and reduce manual effort.
ML Ops Management:
- Model Deployment: Deploy machine learning models into production environments, ensuring versioning, deployment automation, and integration with ML pipelines.
- Monitoring and Logging: Set up monitoring systems to track model performance metrics, log outputs, and detect anomalies or issues.
Dashboard and API Development:
- Front-End Development: Develop interactive dashboards and user interfaces to visualize data insights and model outputs.
- API Development: Design and implement APIs to expose data and ML models, enabling seamless integration with other applications and systems.
Collaboration with AI Engineers:
- Cross-Functional Collaboration: Work effectively with AI Engineers, Software Engineers, and business stakeholders to understand their data requirements, translate them into technical solutions, and optimize data access for AI/ML model development.
- Communication: Convey complex technical concepts and solutions effectively to non-technical stakeholders and team members.
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