AWS Data Engineer

Toronto, ON- 2 days a week Onsite

Long Term contract(6 Months to start with)

We are seeking a highly skilled and experienced Data Engineer to join our dynamic team. In this role, you will be responsible for designing, building, and maintaining scalable and reliable data pipelines and infrastructure on AWS. You will play a critical role in enabling our data-driven decision-making processes by ensuring the availability and quality of our data. The ideal candidate will possess a strong background in AWS cloud services, Python, SQL, PySpark, Airflow, and infrastructure as code (CDK). Experience with DevOps practices is a significant plus.

Responsibilities

Data Pipeline Development: Design, develop, and maintain robust and scalable data pipelines using Python, PySpark, and Airflow to ingest, process, and transform large datasets.

Cloud Infrastructure (AWS): Architect, build, and manage data infrastructure on AWS using services like S3, EC2, EMR, Redshift, Glue, and Lambda.

Infrastructure as Code (CDK): Implement and manage infrastructure as code using AWS CDK to ensure consistency, repeatability, and scalability of our data platform.?

Database Management: Design and optimize database schemas and queries using SQL for efficient data storage and retrieval.

Data Quality and Testing: Implement comprehensive unit testing strategies to ensure data quality and pipeline reliability.

Performance Optimization: Identify and resolve performance bottlenecks in data pipelines and infrastructure.

Collaboration: Work closely with data scientists, analysts, and other engineers to understand data requirements and deliver effective solutions.

Documentation: Create and maintain thorough documentation of data pipelines, infrastructure, and processes.

Monitoring and Alerting: Implement monitoring and alerting systems to ensure the health and performance of data pipelines and infrastructure.? DevOps (Preferred): Contribute to DevOps practices, including CI/CD pipelines, automated deployments, and infrastructure monitoring.

Qualifications

Experience: 6-10 years of experience in data engineering or a related field.

Programming Languages: Strong proficiency in Python and SQL. Extensive experience with PySpark for distributed data processing.

Workflow Orchestration: Proven experience with Airflow for scheduling and managing data pipelines.? Cloud Computing (AWS): Deep understanding of AWS cloud services and best practices for data engineering.

Infrastructure as Code (CDK): Hands-on experience with AWS CDK for infrastructure provisioning and management.

Database Systems: Solid understanding of relational and NoSQL databases.

Testing: Strong understanding and implementation of unit testing strategies.

Problem-Solving: Excellent analytical and problem-solving skills.

Communication: Strong communication and collaboration skills.

DevOps (Preferred): Knowledge of DevOps principles and practices, including CI/CD, containerization (Docker), and orchestration