Jobs → Data & Analytics → AWS Data Engineer

About the Role We are looking for a hands-on AWS Data Engineer with Data Science skills to design, build, and maintain scalable cloud data pipelines while operationalizing machine learning models and data science workloads. Key Responsibilities • Data Pipeline & Lakehouse Engineering: Design, build, and optimize end-to-end batch and streaming data pipelines using Amazon S3, AWS Glue, Amazon EMR, and Kinesis. • Data Science & ML Integration: Operationalize and deploy machine learning models (scikit-learn, PyTorch, or TensorFlow) into production workflows using SageMaker and Lambda. • Feature Engineering & Warehousing: Manage large-scale data storage and analytics using Amazon Redshift and Athena, creating clean feature sets for data science models. • Orchestration & Automation: Build robust workflow orchestration using Apache Airflow, MWAA (Managed Workflows for Apache Airflow), or AWS Step Functions. • Data Governance & Quality: Ensure data integrity, pipeline monitoring, and security compliance using IAM, Lake Formation, and CloudWatch Requirements • Cloud & Services: 7+ years of hands-on experience with core AWS big data services (S3, Glue, Redshift, Athena, EMR, SageMaker). • Programming: Advanced proficiency in Python and SQL; experience with PySpark or Scala for big data processing. • Data Science Acumen: Solid understanding of machine learning lifecycles, feature store concepts, and model deployment/inference patterns. • DevOps & Tools: Experience with Docker, Git, CI/CD pipelines, and Infrastructure as Code (Terraform or AWS CDK).