Senior Data/ML Engineer (AWS)

Accountabilities:

In this role, you will be responsible for designing and delivering scalable data and ML solutions that support enterprise-grade analytics and AI use cases.

• Design and implement multi-zone data lake architectures on AWS using S3, including raw, curated, and analytics-ready layers aligned with enterprise requirements.


• Build and maintain batch and real-time data pipelines using services such as AWS Glue, Kinesis, and Step Functions to integrate diverse data sources.


• Develop ETL workflows, data transformations, and metadata management frameworks using AWS Glue Data Catalog and related tools.


• Deploy and operationalize ML models using Amazon SageMaker for use cases such as prediction, scoring, and segmentation.


• Integrate generative AI capabilities using Amazon Bedrock to enable intelligent automation, personalization, and enrichment workflows.


• Support data migration initiatives from Azure to AWS, including schema mapping, validation, reconciliation, and performance optimization.


• Implement data governance, security, and access controls using AWS Lake Formation and ensure compliance with data standards.


• Collaborate with cross-functional teams to define architecture, maintain documentation, and ensure data quality across all pipelines and outputs.

Requirements

• 5+ years of experience in data engineering or ML engineering, with at least 2+ years working extensively on AWS.


• Strong proficiency in Python and SQL with hands-on experience in building scalable data pipelines.


• Deep knowledge of AWS services including S3, Glue, Athena, Kinesis, Lambda, and Step Functions.


• Experience with Amazon SageMaker for training, tuning, deploying, and monitoring ML models in production.


• Working knowledge of Amazon Bedrock or other generative AI frameworks for enterprise use cases.


• Experience designing and maintaining data lake architectures with strong governance and security models.


• Familiarity with Azure data platforms and cloud migration projects is highly desirable.


• Strong understanding of data modeling, feature engineering, and ML integration best practices.


• Excellent problem-solving, communication, and collaboration skills in Agile environments.

• Competitive compensation package aligned with experience and expertise


• Fully remote work opportunity


• Exposure to cutting-edge AWS data and AI/ML technologies


• Opportunity to work on large-scale enterprise data transformation programs


• Collaborative and cross-functional global engineering environment


• Career growth in advanced data engineering and machine learning domains


• Flexible work culture supporting autonomy and ownership

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