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Senior Data Engineer

3 months ago

Old Toronto Ontario, CA Healthcare of Ontario Pension Plan Trust Fund Company Full time

High-performance, people-focused culture.

Our commitment that equity, diversity, and inclusion are fundamental to our work environment and business success, which helps employees feel valued and empowered to be their authentic selves.

Learning and development initiatives, including workshops, Speaker Series events, and access to LinkedIn Learning, that support employees’ career growth.

Membership in HOOPP’s world-class defined benefit pension plan, which can serve as an important part of your retirement security.

Competitive, 100% company-paid extended health and dental benefits for permanent employees, including coverage supporting our team's diversity and mental health (e.g., gender affirmation, fertility and drug treatment, psychological support benefits of $2,500 per year, and newly extended maternity/parental leave top of 26 weeks).

Optional post-retirement health and dental benefits subsidized at 50%.

Yoga classes, meditation workshops, nutritional consultations, and wellness seminars.

Access to an annual wellness reimbursement program for health and wellness-related expenses for permanent and temporary employees.

The opportunity to make a difference and help take care of those who care for us, by providing a financially secure retirement for Ontario healthcare workers.

Job Summary

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 in transforming data into actionable insights.

What you will do:
  1. Problem-Solving: Approach technical challenges with an entrepreneurial mindset, unblocking the team for faster prototyping and experimentation of data solutions.
  2. Strive for Efficiency: Test new tools and technologies to generate ideas around AI/ML solutions, enhancing the team’s capability for faster experimentation.
  3. Data Engineering Expertise: Design, build, and maintain scalable data pipelines that are robust, reliable, and efficient.
  4. ETL Processes: Implement ETL processes to acquire, transform, and load data from various sources into data storage solutions.
  5. Scalability: Ensure data pipelines are scalable to handle large volumes of data and perform efficiently under varying workloads.
Data Acquisition and Cleaning
  1. Data Integration: Integrate data from diverse sources (SQL, NoSQL, APIs, flat files, etc.) while maintaining data integrity and quality.
  2. Data Cleansing: Clean and preprocess data to address inconsistencies, missing values, and outliers, ensuring data readiness for analysis and modeling.
  3. Automated Data Processing: Utilize tools and techniques for automating data acquisition and cleaning processes to streamline workflows and reduce manual effort.
ML Ops Management
  1. Model Deployment: Deploy machine learning models into production environments, ensuring versioning, deployment automation, and integration with ML pipelines.
  2. Monitoring and Logging: Set up monitoring systems to track model performance metrics, log outputs, and detect anomalies or issues.
Dashboard and API Development
  1. Front-End Development: Develop interactive dashboards and user interfaces to visualize data insights and model outputs.
  2. API Development: Design and implement APIs to expose data and ML models, enabling seamless integration with other applications and systems.
Collaboration with AI Engineers
  1. 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.
  2. Communication: Convey complex technical concepts and solutions effectively to non-technical stakeholders and team members.
Programming Languages:

Proficiency in Python and SQL for data manipulation, extraction, and analysis. Experience in at least one object-oriented language to develop web applications.

Databases:

Experience with both SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Snowflake) and data formats like Apache Parquet for data storage and optimization.

ETL Tools and Data Pipelines:

Familiarity with ETL processes and tools such as Apache Airflow or AWS Glue.

Cloud Platforms:

Expertise in cloud platforms and services such as AWS for deploying and managing data infrastructure.

MLOps Tools:

Experience with MLOps tools like AWS Sagemaker for managing machine learning workflows.

What you bring:
  1. Bachelor's degree or higher in Computer Science, Engineering, Mathematics, Statistics, or a related field.
  2. Overall 9+ years of experience, including graduate school.
  3. At least 5+ years of experience in a data engineering or software engineering role, with a proven track record in designing and implementing data solutions.
  4. Proficiency in Python and SQL for data manipulation, extraction, and analysis.
  5. Experience with both SQL and NoSQL databases, with the ability to design and optimize database schemas.
  6. Experience as a full-stack software engineer is highly advantageous.
  7. Familiarity with cloud platforms and services such as AWS, Azure, GCP, etc., for deploying and managing data infrastructure.
  8. Knowledge of capital markets and financial data is a significant plus.
  9. Experience in MLOps and familiarity with tools like AWS Sagemaker, MLFlow, etc., is advantageous.
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