AWS Data Engineer

Location: Toronto (light hybrid) Duration: 6mo with very possible extension Hr/week: 37.5 Requirements: 3-5 years of experience in data engineering, with a focus on AWS Data Engineering tools. ETL processes and tools (e.g., AWS Glue, PySpark, Apache Airflow). Event streaming technologies (e.g., Apache Kafka, AWS Kinesis). Strong programming in Python, Java, or Scala. SQL and database management systems (e.g., PostgreSQL, MySQL). Strong communication skills and the ability to convey complex technical concepts to non-technical stakeholders. Experience with Agile Scrum and XP methodologies. Experience with Test Driven Development (TDD). Bachelor's degree in Computer Science, IT or a related field. Professional certification in AWS Data Engineering Data warehousing solutions (e.g., Amazon Redshift, Snowflake) - nice to have Knowledge of data governance and security best practices - nice to have Experience with MS Azure and MS Fabric - nice to have Responsibilities: Design, develop, and maintain scalable data pipelines using AWS Data Engineering tools (e.g., AWS Glue, AWS Lambda, Amazon Redshift). Implement ETL processes to extract, transform, and load data from various sources into data warehouses and data lakes. Develop and manage event streaming solutions using tools such as Apache Kafka, AWS Kinesis, or similar technologies. Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet business needs. Optimize and troubleshoot data pipelines to ensure high performance and reliability. Monitor and maintain data quality and integrity across all data systems. Stay up-to-date with the latest trends and best practices in data engineering and AWS technologies. Work within Agile Scrum and XP methodologies, participating in sprint planning, daily stand-ups, and retrospectives to ensure timely delivery of projects. Apply Test Driven Development (TDD) practices to ensure high-quality code and robust data solutions.