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AWS Data Engineer
- Toronto, ON, Canada
Contract
This opening has closed.
The original listing is kept below for reference.
Job details
- Job type
- Contract
- Closing date
- Experience
- 8+ years of strong Data Engineering experience.
- Posting language
- English
- Posted
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Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
Role: AWS Data Engineer
Location: Toronto, ON – Hybrid
Contract Role
Job Summary
Key Responsibilities
- Design and develop scalable data pipelines and ETL/ELT processes using AWS services.
- Develop complex and optimized SQL queries for data extraction, transformation, and analysis.
- Build and maintain data solutions using AWS Glue, S3, Lambda, Athena, Redshift, and EMR.
- Develop data transformation workflows using Python/PySpark.
- Perform data integration from multiple sources into AWS data platforms.
- Implement data quality, validation, transformation, and reconciliation processes.
- Optimize data pipelines and SQL queries for performance and cost efficiency.
- Work with business and analytics teams to understand data requirements.
- Troubleshoot data pipeline failures and resolve data-related issues.
- Implement automated testing and monitoring for data pipelines.
- Follow data governance, security, and compliance best practices.
Required Skills
- 8+ years of strong Data Engineering experience.
- Strong hands-on experience with AWS Cloud data services.
- Excellent SQL skills, including complex queries, joins, CTEs, window functions, and query optimization.
- Strong experience with AWS Glue and Amazon S3.
- Experience with Amazon Redshift and/or Athena.
- Strong Python and/or PySpark experience.
- Experience developing ETL/ELT pipelines.
- Experience with data warehousing and data modeling.
- Strong understanding of batch and real-time data processing.
- Experience with Git and CI/CD for data engineering projects.
Preferred Skills
- AWS Lambda
- Amazon EMR
- AWS Step Functions
- Amazon Kinesis
- Apache Spark
- Kafka
- Airflow
- Data Lake / Data Warehouse architecture
- AWS Lake Formation / Glue Data Catalog
Data quality and governance tools
More about this job
Responsibilities
Design, build, and optimize scalable AWS data pipelines and ETL/ELT workflows, integrating data from multiple sources using services such as Glue, S3, Lambda, Athena, Redshift, and EMR. Ensure data quality, security, governance, testing, and monitoring while collaborating with business and analytics teams and resolving pipeline issues.
Requirements
Requires 8 or more years of data engineering experience, strong hands-on AWS data services experience, advanced SQL, and proficiency in Python and/or PySpark. Candidates should also have experience with ETL/ELT pipelines, data warehousing and modeling, batch and real-time processing, and Git and CI/CD.
Skills
- Data Engineering
- AWS
- SQL
- AWS Glue
- Amazon S3
- Amazon Redshift
- Amazon Athena
- Python
- PySpark
- ETL/ELT
- Data Warehousing
- Data Modeling
- Batch Processing
- Real-Time Data Processing
- Git
- CI/CD
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Engineering
Keywords
- AWS
- Data Engineering
- Data Pipelines
- ETL/ELT
- SQL
- AWS Glue
- Amazon S3
- AWS Lambda
- Amazon Athena
- Amazon Redshift
- Amazon EMR
- Python
- PySpark
- Data Integration
- Data Quality
- Data Validation
- Data Reconciliation
- Data Warehousing
- Data Modeling
- Batch Processing
- Real-Time Data Processing
- Git
- CI/CD
- AWS Step Functions
- Amazon Kinesis
- Apache Spark
- Kafka
- Airflow
- Data Lake
- AWS Lake Formation