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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
More about this job
Responsibilities
Design, build, and optimize AWS-based data pipelines and ETL/ELT workflows, integrating data from multiple sources and applying data quality, validation, and reconciliation processes. Collaborate with business and analytics teams, troubleshoot pipeline issues, and implement automated testing, monitoring, governance, security, and compliance practices.
Requirements
Requires 8+ years of data engineering experience, strong hands-on AWS data services experience, advanced SQL, and experience with AWS Glue, S3, Redshift and/or Athena, and Python and/or PySpark. Candidates should also have experience with ETL/ELT, data warehousing and modeling, batch and real-time processing, and Git and CI/CD; AWS Lambda, EMR, Step Functions, Kinesis, Spark, Kafka, Airflow, and data lake architecture are preferred.
Skills
- AWS Data Services
- SQL
- AWS Glue
- Amazon S3
- Amazon Redshift
- Amazon Athena
- Python
- PySpark
- ETL/ELT Pipelines
- Data Warehousing
- Data Modeling
- Batch and Real-Time Processing
- Git
- CI/CD
- Data Quality
- Data Pipeline Optimization
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Engineering
Keywords
- AWS
- AWS Glue
- Amazon S3
- AWS Lambda
- Amazon Athena
- Amazon Redshift
- Amazon EMR
- AWS Step Functions
- Amazon Kinesis
- AWS Lake Formation
- AWS Glue Data Catalog
- SQL
- Python
- PySpark
- Apache Spark
- Kafka
- Airflow
- ETL/ELT
- Data Pipelines
- Data Integration
- Data Quality
- Data Validation
- Data Reconciliation
- Data Warehousing
- Data Modeling
- Data Lake Architecture
- Batch Processing
- Real-Time Processing
- Git
- CI/CD