Company
Technology
SeniorData/MLEngineer(AWS)
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data/ML Engineer (AWS). Skills: Data engineering, ML engineering, AWS, Data lake architecture. Design scalable data solutions. Deliver scalable ML solutions”
Industry & Context.
Problem-solving
What They're Looking For.
Must Have
5+ years of experience in data engineering or ML engineering, 2+ years working extensively on AWS, Proficiency in Python, Proficiency in SQL, Experience designing and maintaining data lake architectures
Nice to Have
Familiarity with Azure data platforms, Experience with cloud migration projects, Understanding of data modeling, Understanding of feature engineering, Understanding of ML integration best practices
What You'll Do.
Design scalable data solutions
Deliver scalable ML solutions
Design data lake architectures on AWS
Implement data lake architectures
Maintain data pipelines
Develop ETL workflows
Develop data transformations
Develop metadata management frameworks
Deploy ML models using SageMaker
Operationalize ML models using SageMaker
Integrate generative AI capabilities
Support data migration initiatives
Implement data governance
Implement security controls
Implement access controls
Maintain documentation
How You'll Work.
Team & Collaboration
Cross-functional teams
Process & Methodology
Agile environments
Full Job Description
## 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: The ideal candidate brings strong experience in cloud data engineering and applied machine learning within AWS environments. 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
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