Senior Data / ML Engineer

• Design, develop, and optimize large-scale ETL/ELT pipelines using AWS services such as EMR (Spark), Glue, Lambda, and Step Functions.
• Orchestrate complex data workflows with Apache Airflow (Amazon MWAA or self-managed), ensuring reliability, observability, and SLA adherence.
• Architect and manage data storage solutions across Amazon S3 (data lake), Redshift (data warehouse), and RDS (relational databases), applying best practices for partitioning, compression, and cost optimization.
• Build and maintain containerized data applications and microservices using Docker and Amazon ECS/Fargate, including CI/CD automation.
• Develop event-driven and serverless data processing solutions with AWS Lambda, SQS, SNS, and EventBridge.
• Leverage AI-powered coding assistants and IDE integrations (e.g., Kiro, Cursor, Claude Code) to accelerate development, code review, and documentation.
• Implement data quality frameworks, monitoring, and alerting to ensure data integrity across all pipelines.
• Collaborate with Data Scientists to productionize ML models and feature pipelines.
• Define and enforce data governance, security, and access-control policies in line with organizational and regulatory standards.
• Contribute to infrastructure-as-code initiatives using Terraform, CloudFormation, or CDK.

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Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• 5+ years of professional experience in data engineering, with at least 3 years of hands-on AWS production workloads.
• 3+ years of experience with AWS Services: EMR (Spark/Hadoop), Apache Airflow, S3, Redshift, RDS, Lambda, Sagemaker and ECS.
• Solid experience with Docker (building, optimizing, and deploying containers) and container orchestration.
• Proficiency in Python and SQL; experience with Typescript is a plus.
• Demonstrated ability to use AI-assisted development tools within modern IDEs for rapid prototyping, code generation, testing and debugging.
• Strong understanding of data modeling, data governance, and data security best practices.
• AWS certifications such as AWS Certified Data Analytics - Specialty or AWS Certified Solutions Architect are a plus.
• Experience with infrastructure-as-code tools (Terraform, CloudFormation, CDK).
• Exposure to ML Ops workflows, feature stores, or model serving pipelines (e.g., SageMaker).
  • Knowledge of cost-optimization strategies for large-scale AWS data workloads.