Responsibilities:

- Designed and developed scalable

AWS Data Lake solutions for ingesting, processing, and analyzing enterprise data.

- Built and maintained ETL/ELT pipelines using

AWS Glue

, PySpark, and AWS Lambda.

- Developed batch and real time data pipelines integrating multiple source systems into Amazon S3 data lakes.

- Implemented data transformation, cleansing, and enrichment processes using AWS Glue Jobs and Glue Workflows.

- Managed metadata catalogs using AWS Glue Data Catalog and optimized data discovery capabilities.

- Designed data ingestion frameworks leveraging S3, Lambda, EventBridge, and Step Functions.

- Created analytical datasets for reporting and business intelligence consumption.

- Optimized Glue jobs for performance, scalability, and cost efficiency.

- Implemented data quality, governance,



and monitoring controls across pipelines.

- Worked with Redshift, Athena, and Lake Formation to support data analytics and governance initiatives.

- Developed CI/CD processes for data pipelines and automated deployment across environments.

- Collaborated with business stakeholders, application teams, and data architects to define data integration requirements.

Required Skills:

- AWS Glue

- AWS Data Lake

- Amazon S3

- PySpark, Python

- Athena, Redshift

- Lambda, Step Functions

- SQL & Data Modeling

- ETL/ELT Development

- Lake Formation

- Data Governance & Data Quality

- Event Bridge and Data Pipeline Automation

📌 AWS Data Engineer (Canada)
🏢 TechDoQuest
📍 Canada