AWS Data Engineer with DS skills
PeopleNTech · Alexandria, VA · Hybrid
Strong hiring record · median 11 days open
Hiring signal
Actively hiring
Still listed on the employer's own board · publishes a salary range · How we score
All reasons
In its favour
- Still listed on the employer's own board
- Publishes a salary range
Indent
SF_OP_211336-2
Role: AWS Data Engineer with DS skills
Location: Cambridge, MA (Hybrid- once a week)
Max Rate: $64- 66/hr
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 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)
Skills
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