Data Engineering & Development 
• Design, develop, and maintain scalable data pipelines using Python and SQL. 
• Build robust ETL/ELT frameworks for structured and unstructured data processing. 
• Develop data ingestion solutions from APIs, databases, cloud storage, and external 
file sources. 
• Perform data cleansing, transformation, enrichment, validation, and quality 
assurance activities. 
• Design reusable and maintainable data processing components. 
Cloud & Big Data Platforms 
• Develop and support cloud-native data solutions on AWS and/or Azure. 
• Build and optimize data workflows using services such as AWS Glue, EMR, Lambda, 
S3, RDS or Azure Data Factory, Databricks, Synapse, and ADLS. 
• Work with large-scale datasets and distributed data processing frameworks. 
Data Warehouse & Analytics 
• Design and maintain data models, data marts, and analytical datasets. 
• Support enterprise reporting and self-service analytics initiatives. 
• Optimize database performance and query execution. 
• Collaborate with Power BI teams on semantic models and reporting datasets. 
Data Quality & Operations 
• Implement data validation frameworks and quality controls. 
• Develop monitoring, alerting, logging, and operational support processes. 
• Troubleshoot production data pipeline failures and performance issues. 
• Support ongoing maintenance and improvements of enterprise data platforms. 
Engineering Excellence 
• Follow coding standards, peer reviews, and software engineering best practices. 
• Implement CI/CD pipelines for data engineering workloads. 
• Maintain source control repositories using Git. 
• Create technical documentation and knowledge-sharing materials.