Jobs → Data & Analytics → AWS Data Engineer

About the Role We are looking for an experienced AWS Data Engineer with strong hands-on Amazon SageMaker to join our team. Key Responsibilities • Design and maintain scalable batch and streaming data pipelines using AWS Glue, EMR, Lambda, Kinesis/MSK, and Airflow • Develop data lake and warehouse solutions using Amazon S3, Lake Formation, Athena, and Redshift • Build ML-ready datasets and manage features using SageMaker Processing, Data Wrangler, and Feature Store • Develop end-to-end ML workflows using SageMaker Pipelines, training, tuning, evaluation, and Model Registry • Deploy and monitor ML models using SageMaker endpoints and CloudWatch • Implement data governance, security, data quality, and lineage controls • Automate infrastructure and deployments using Terraform, CloudFormation/CDK, and CI/CD pipelines • Optimise data and ML workloads for performance, scalability, reliability, and cost Requirements • 7+ years of experience in Data Engineering, with strong hands-on AWS expertise • Proven experience with Amazon SageMaker, including Pipelines, Processing, Feature Store, training jobs, and endpoints • Strong proficiency in Python, SQL, and PySpark • Extensive experience with AWS services such as S3, Glue, Athena, Redshift, Lambda, IAM, and KMS • Experience with workflow orchestration using Airflow/MWAA or Step Functions • Hands-on experience with Infrastructure as Code and CI/CD using Terraform, CloudFormation, or CDK • Strong understanding of data modelling, partitioning, and columnar formats such as Parquet, Iceberg, or Delta Lake • Excellent communication and collaboration skills