AWS Data Engineer with Strong SQL, GLUE, Lambda, S3, PySpark, Kafka

Role: AWS Data Engineer With Strong SQL

Location: Toronto, Hybrid

Client: Banking Labs/BMO

Experience Level: 8-12 yrs

Job Summary

We are seeking a highly skilled Data Engineer with deep expertise in SQL (including stored procedures) and AWS-based data engineering solutions. You will be responsible for designing, building, and optimizing scalable data pipelines and systems that enable analytics, reporting, and data-driven decision-making across the organization.

Key Responsibilities

• Develop, and optimize complex SQL queries and stored procedures for high-performance data processing and transformation.
• Build and maintain scalable ETL/ELT pipelines on AWS using services such as Glue, Lambda, S3, Athena, Redshift, Kafka, Airflow, and Step Functions.
• Implement and maintain data integration solutions from various data sources into a centralized data lake or data warehouse.
• Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and deliver reliable solutions.
• Monitor and troubleshoot data pipelines to ensure data accuracy, timeliness, and reliability.
• Apply data governance and security best practices in all development work.

Skills Required

• Expert-level proficiency in SQL, including performance tuning, writing complex joins, and stored procedures in databases.
• Experience with AWS Data Engineering tools
• AWS Glue
• Amazon Redshift
• AWS Lambda
• Amazon S3
• Athena
• Kafka
• Step Functions
• Experience with data modeling, ETL orchestration, and data pipeline development in AWS.
• Familiarity with version control systems (e.g., Git) and CI/CD for data workflows.
• Airflow
• Experience with Python and PySpark