Role: Lead AWS Data Engineer- SQL Expert , ETL, Python
Location: Toronto
Hybrid
AWS, SQL, OLAP, OLTP, ETL , PYTHON
Responsibilities:
Design, develop, and maintain robust data pipelines using AWS services (Glue, Lambda, S3, Redshift, EMR, etc.)
Collaborate with data scientists, analysts, and business stakeholders to gather requirements and build ETL/ELT workflows.
Optimize data workflows for scalability, reliability, and performance.
Ensure data quality, governance, and security standards are met.
Monitor and troubleshoot data pipeline issues.
Required Skills:
8+ years of experience in data engineering, preferably in a cloud environment.
Strong hands-on experience with AWS services: Glue, Redshift, S3, Lambda, EMR, Athena.
Proficient in Python and SQL.
Experience with data modeling, warehousing concepts, and data lakes.
Familiarity with orchestration tools like Airflow or Step Functions.
Knowledge of CI/CD practices and version control (e.g., Git).
Nice to Have:
Experience with real-time data processing (e.g., Kinesis, Kafka).
Knowledge of Terraform or CloudFormation for infrastructure as code.
Familiarity with DevOps practices and tools.
AWS, SQL, OLAP, OLTP, ETL , PYTHON
Responsibilities:
Design, develop, and maintain robust data pipelines using AWS services (Glue, Lambda, S3, Redshift, EMR, etc.)
Collaborate with data scientists, analysts, and business stakeholders to gather requirements and build ETL/ELT workflows.
Optimize data workflows for scalability, reliability, and performance.
Ensure data quality, governance, and security standards are met.
Monitor and troubleshoot data pipeline issues.
Required Skills:
8+ years of experience in data engineering, preferably in a cloud environment.
Strong hands-on experience with AWS services: Glue, Redshift, S3, Lambda, EMR, Athena.
Proficient in Python and SQL.
Experience with data modeling, warehousing concepts, and data lakes.
Familiarity with orchestration tools like Airflow or Step Functions.
Knowledge of CI/CD practices and version control (e.g., Git).
Nice to Have:
Experience with real-time data processing (e.g., Kinesis, Kafka).
Knowledge of Terraform or CloudFormation for infrastructure as code.
Familiarity with DevOps practices and tools.
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