The Senior Data Engineer is responsible for designing, building, and optimizing scalable data platforms and pipelines that support analytics, machine learning, and business intelligence initiatives. This role requires deep expertise in AWS Redshift, SageMaker AI, Quick Suite (Amazon QuickSight), Apache Airflow and Python, with a strong emphasis on performance, reliability, and data governance.

• Design, develop, andmaintainscalable data pipelines using Python and AWS-native services.

• Architect andoptimizedata warehouse solutions in AWS Redshift, including schema design, workload management, and performance tuning.

• Implement ELT/ETL processes to ingest, transform, and curate structured and semi-structured data.

• Ensure data integrity, quality, and security across all data platforms.

• Develop andmaintaincloud-native data solutionsleveragingAWS services (e.g., Redshift, S3, Glue, Lambda, SageMaker,Kinesis, RDS,IAM).

• OptimizeRedshift clusters for cost efficiency and high performance.

• Implement CI/CD pipelines and infrastructure-as-code practices for data systems.

• Partner with data scientists to operationalize machine learning models using SageMaker AI.

• Build data pipelines that support model training, validation, and deployment.

• Implement feature engineering and feature store best practices.

• Monitor andoptimizemodel performance in production environments.

• Enable and support analytics and reporting solutions using AmazonQuickSight(Quick Suite).

• Develop curated datasets and semantic layers to support self-service analytics.

• Ensure data models are structured to support performance and scalability in BI tools.

• Establish and enforce data engineering standards, coding best practices, and documentation.

• Implement monitoring, logging, andalerting fordata pipelines and warehouse performance.

• Ensure compliance with security, privacy, and regulatory requirements.

• Mentor junior engineers and provide technical leadership across data initiatives.