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AWS Data Engineer with DS skills

PeopleNTech · Alexandria, VA · Hybrid

Strong hiring record · median 11 days open

100100

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Still listed on the employer's own board · publishes a salary range · How we score

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  • Still listed on the employer's own board
  • Publishes a salary range

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AWS Data Engineer with DS skills at PeopleNTech. We’ll keep it in your applied jobs, so you can see it later and be warned before applying to the same employer twice.

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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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