Company
Technology
SeniorData/MLEngineer(AWS)
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“Senior Data/ML Engineer (AWS). Skills: Data Lake, Data Pipelines, MLOps, Generative AI. The Senior Data/ML Engineer designs and delivers scalable data and ML solutions on AWS, including multi-zone data lake architectures using S3 and batch/real-time data pipelines with services like Glue and Kinesis. This role involves deploying ML models with SageMaker, integrating generative AI via Bedrock, and supporting Azure to AWS data migrations while ensuring data governance and quality.”
Industry & Context.
data modeling; feature engineering; ML integration
What They're Looking For.
Must Have
5+ years of experience in data engineering or ML engineering, with at least 2+ years working extensively on AWS. 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 governance and security models. Excellent problem-solving, communication, and collaboration skills in Agile environments.
Nice to Have
Familiarity with Azure data platforms and cloud migration projects is highly desirable. Understanding of data modeling, feature engineering, and ML integration best practices.
What You'll Do.
The Senior Data/ML Engineer designs and delivers scalable data and ML solutions on AWS
including multi-zone data lake architectures using S3 and batch/real-time data pipelines with services like Glue and Kinesis. This role involves deploying ML models with SageMaker
integrating generative AI via Bedrock
and supporting Azure to AWS data migrations while ensuring data governance and quality.
How You'll Work.
Team & Collaboration
Collaborate with cross-functional teams to define architecture, maintain documentation, and ensure data quality across all pipelines and outputs.
Communication Scope
communication
Process & Methodology
Agile
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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