AWS Data Engineer with Data Scientist skills
- 🇺🇸 United States
- On-site
- 10 hours ago
- AWS
- Machine Learning
- AWS Glue
- EMR
- Kinesis
- scikit-learn
- PyTorch
- TensorFlow
- Redshift
- Athena
- Apache Airflow
- Step Functions
- IAM
- CloudWatch
- Python
- SQL
- PySpark
- Scala
- Devops
- Docker
- Git
- CI/CD
- IaC
- Terraform
- AWS CDK
We have an urgent requirement of 1 AWS Data Engg with Data Scientist skills, for our customer. See required details below:
· Indent ID:<SF_OP_211336-2>
· Detailed JD:
A hands-on AWS Data Engineer with Data Science skills designs, builds, and maintains scalable cloud data pipelines while operationalizing machine learning models and data science workloads.
Job Description Overview
·Role: Hands-On AWS Data Engineer with Data Science Skills
·Core Mission: Bridge the gap between raw data infrastructure and production-ready machine learning by building reliable data lakes, feature stores, and automated ML pipelines on AWS.
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
Required Qualifications & Skills
·Cloud & Services: 7+ years of hands-on experience with core AWS big data services (S3, Glue, Redshift, Athena, EMR, SageMaker).
·Programming: Advanced proficiency inPython andSQL; experience withPySpark 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)
· Sell Rate: $74-76/hr.
· Client Full Address:500 Kendall Street, Cambridge, MA 02142
· Remote / WFO / Hybrid:Remote(preferably from Cambridge, MA)
· Hire Type (FTE/Contract):Contract
· Project Duration:8 Months
· Expected Start Date:26 Oct 26
· No. of client interviews for this role:1 to 2
AWS Data Engineer with Data Scientist skills · PeopleNTech LLC